With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
Intelligent flood disaster warning on the fly: developing IoT-based managemen...journalBEEI
The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network
is used.
New Research Articles 2019 July Issue International Journal of Artificial Int...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas
Coupling Australia’s Researchers to the Global Innovation EconomyLarry Smarr
08.10.10
Fifth Lecture in the
Australian American Leadership Dialogue Scholar Tour
University of Queensland
Title: Coupling Australia’s Researchers to the Global Innovation Economy
Brisbane, Australia
In this video from the HPC User Forum at Argonne, Dr. Brett Bode from NCSA presents: Research on Blue Waters.
"Blue Waters is one of the most powerful supercomputers in the world and is one of the fastest supercomputers on a university campus. Scientists and engineers across the country use the computing and data power of Blue Waters to tackle a wide range of challenging problems, from predicting the behavior of complex biological systems to simulating the evolution of the cosmos."
Watch the video: https://wp.me/p3RLHQ-kYx
Learn more: http://www.ncsa.illinois.edu/enabling/bluewaters
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Design and Implementation of Smart congestion control systemdbpublications
The frequent traffic jams at major junctions
call for an efficient traffic management
system in place. The resulting wastage of
time and increase in pollution levels can be
eliminated on a city-wide scale by these
systems.
The project proposes to implement
an intelligent traffic controller using real
time image processing. The image
sequences from a camera are analyzed using
thresholding method to find the density.
Subsequently, the number of vehicles at
the intersection is evaluated and traffic is
efficiently managed. The project also
proposes to implement a real-time
emergency vehicle detection system. In case
an emergency vehicle is detected, the lane is
given priority over all the others. Hardware
control is done by microcontroller.
This is a presentation of the JGrass-newAGE system held in Potenza on February 24 20117. It contains an overview of concepts, ideas, behing JGrass-NewAGE ans shows some achievements in a critical manner.
Top 5 most viewed articles from academia in 2019 - gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
Intelligent flood disaster warning on the fly: developing IoT-based managemen...journalBEEI
The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network
is used.
New Research Articles 2019 July Issue International Journal of Artificial Int...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas
Coupling Australia’s Researchers to the Global Innovation EconomyLarry Smarr
08.10.10
Fifth Lecture in the
Australian American Leadership Dialogue Scholar Tour
University of Queensland
Title: Coupling Australia’s Researchers to the Global Innovation Economy
Brisbane, Australia
In this video from the HPC User Forum at Argonne, Dr. Brett Bode from NCSA presents: Research on Blue Waters.
"Blue Waters is one of the most powerful supercomputers in the world and is one of the fastest supercomputers on a university campus. Scientists and engineers across the country use the computing and data power of Blue Waters to tackle a wide range of challenging problems, from predicting the behavior of complex biological systems to simulating the evolution of the cosmos."
Watch the video: https://wp.me/p3RLHQ-kYx
Learn more: http://www.ncsa.illinois.edu/enabling/bluewaters
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Design and Implementation of Smart congestion control systemdbpublications
The frequent traffic jams at major junctions
call for an efficient traffic management
system in place. The resulting wastage of
time and increase in pollution levels can be
eliminated on a city-wide scale by these
systems.
The project proposes to implement
an intelligent traffic controller using real
time image processing. The image
sequences from a camera are analyzed using
thresholding method to find the density.
Subsequently, the number of vehicles at
the intersection is evaluated and traffic is
efficiently managed. The project also
proposes to implement a real-time
emergency vehicle detection system. In case
an emergency vehicle is detected, the lane is
given priority over all the others. Hardware
control is done by microcontroller.
This is a presentation of the JGrass-newAGE system held in Potenza on February 24 20117. It contains an overview of concepts, ideas, behing JGrass-NewAGE ans shows some achievements in a critical manner.
Top 5 most viewed articles from academia in 2019 - gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
IEEE CASE 2016 On Avoiding Moving Objects for Indoor Autonomous QuadrotorsPeter SHIN
Abstract - A mini quadrotor can be used in many applica- tions, such as indoor airborne surveillance, payload delivery, and warehouse monitoring. In these applications, vision-based autonomous navigation is one of the most interesting research topics because precise navigation can be implemented based on vision analysis. However, pixel-based vision analysis approaches require a high-powered computer, which is inappropriate to be attached to a small indoor quadrotor. This paper proposes a method called the Motion-vector-based Moving Objects Detec- tion. This method detects and avoids obstacles using stereo motion vectors instead of individual pixels, thereby substan- tially reducing the data processing requirement. Although this method can also be used in the avoidance of stationary obstacles by taking into account the ego-motion of the quadrotor, this paper primarily focuses on providing our empirical verification on the real-time avoidance of moving objects.
The increased usage of mobile devices caused them to face a large amount of resource, memory and processing speed scarcity. Of all other constraints, energy is the major problem for this to carry out a task. The concept of offloading gets into play for mobile devices i.e., the task or the computation which needs to be performed involving more service in the android systems will be shifted to resourceful server (for ex cloud) and getting back the results done from the cloud. The decision of whether to offload a computation or not will depend on the task accounting to the energy spent by the device while working with the application versus the amount of energy spent by the same device for uploading the task to the cloud and getting the result back from the cloud. As this concept depicts energy is the major constraint for whether to offload a task or not, there is a model called CUCKOO framework which acts as an interface between the cloud and the android environment to support for the task offloading to the cloud. Thus this framework bridges the gap between the smartphones as well as the cloud environment so that computation intensive task can be performed with less amount of energy consumed. In this work two applications are used to detect the amount of energy consumed in the cloud as well as the smartphones namely eyedentify and Photoshoot.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
3d Modelling of Structures using terrestrial laser scanning techniqueIJAEMSJORNAL
In recent times, interest in the study of engineering structures has been on the rise as a result of improvement in the tools used for operations such as, As-built mapping, deformation studies to modeling for navigation etc. There is a need to be able to model structure in such way that accurate needed information about positions of structures, features, points and dimensions can be easily extracted without having to pay physical visits to site to obtain measurement of the various components of structures. In this project, the data acquisition system used is the terrestrial laser scanner, High Definition Surveying (HDS) equipment; the methodology employed is similar to Close Range Photogrammetry (CRP). CRP is a budding technique or field used for data acquisition in Geomatics. It is a subset of the general photogrammetry; it is often loosely tagged terrestrial photogrammetry. The terrestrial laser scanning technology is a data acquisition system similar to CRP in terms of deigning the positioning of instrument and targets, calibration, ground control point, speed of data acquisition, data processing (interior, relative and absolute orientation) and the accuracy obtainable. The aim of this project was to generate the three-dimensional model of structures in the Faculty of Engineering, University of Lagos using High Definition Surveying, the Leica Scan Station 2 HDS equipment was used along with Cyclone software for data acquisition and processing. The result was a 3D view (of point clouds) of the structure that was studied, from which features were measured from the model generated and compared with physical measurement on site. The technology of the laser scanner proved to be quite useful and reliable in generating three dimensional models without compromising accuracy and precision. The generation of the 3D models is the replica of reality of the structures with accurate dimensions and location.
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Links:
http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814
Seven Most tenable applications of AI o Water Resources ManagementMrinmoy Majumder
AI or Artificial Intelligence is a pioneering technique that has enabled the creation of intelligent machines.or smart machines which has the power to self adapt based on the situation presented to it. It requires situations whose response is known and based on this training data set it learns the problems which it has to solve when it is ready. Due to the alarming success with AI in robotics, electronics etc fields the same technique is now used to solve the problems of water resource management.. This ppt shows seven most notable use of AI in water resources-based problems where satisfactory improvement has encouraged further application of the technique.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
08.09.19
Invited Lecture to the Green IT Workshop
Canada-California Strategic Innovation Partnership
Title: Toward Greener Cyberinfrastructure
Palo Alto, CA
Energy efficient clustering using the AMHC (adoptive multi-hop clustering) t...IJECEIAES
IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive MultiHop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed.
Calit2 - CSE's Living Laboratory for ApplicationsLarry Smarr
08.05.27
UCSD CSE 91 - Perspectives in Computer Science (Spring 2008)
Calit2@UCSD
Title: Calit2 - CSE's Living Laboratory for Applications
La Jolla, CA
In this study, we propose situations where cloud is suitable and fog is more compatible, also define some services according to the cloud and fog architecture. We also provide a comparison of task scheduling algorithms of cloud computing and determine that fog is a light weight network so which is the best suitable algorithm for fog architecture on the basis of some attributes. The implementations of fog computing are challenging in today’s computational era; we define some reasons in which fog computing implementation is difficult.
WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline MonitoringIJECEIAES
One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classical wireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, we present WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
IEEE CASE 2016 On Avoiding Moving Objects for Indoor Autonomous QuadrotorsPeter SHIN
Abstract - A mini quadrotor can be used in many applica- tions, such as indoor airborne surveillance, payload delivery, and warehouse monitoring. In these applications, vision-based autonomous navigation is one of the most interesting research topics because precise navigation can be implemented based on vision analysis. However, pixel-based vision analysis approaches require a high-powered computer, which is inappropriate to be attached to a small indoor quadrotor. This paper proposes a method called the Motion-vector-based Moving Objects Detec- tion. This method detects and avoids obstacles using stereo motion vectors instead of individual pixels, thereby substan- tially reducing the data processing requirement. Although this method can also be used in the avoidance of stationary obstacles by taking into account the ego-motion of the quadrotor, this paper primarily focuses on providing our empirical verification on the real-time avoidance of moving objects.
The increased usage of mobile devices caused them to face a large amount of resource, memory and processing speed scarcity. Of all other constraints, energy is the major problem for this to carry out a task. The concept of offloading gets into play for mobile devices i.e., the task or the computation which needs to be performed involving more service in the android systems will be shifted to resourceful server (for ex cloud) and getting back the results done from the cloud. The decision of whether to offload a computation or not will depend on the task accounting to the energy spent by the device while working with the application versus the amount of energy spent by the same device for uploading the task to the cloud and getting the result back from the cloud. As this concept depicts energy is the major constraint for whether to offload a task or not, there is a model called CUCKOO framework which acts as an interface between the cloud and the android environment to support for the task offloading to the cloud. Thus this framework bridges the gap between the smartphones as well as the cloud environment so that computation intensive task can be performed with less amount of energy consumed. In this work two applications are used to detect the amount of energy consumed in the cloud as well as the smartphones namely eyedentify and Photoshoot.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
3d Modelling of Structures using terrestrial laser scanning techniqueIJAEMSJORNAL
In recent times, interest in the study of engineering structures has been on the rise as a result of improvement in the tools used for operations such as, As-built mapping, deformation studies to modeling for navigation etc. There is a need to be able to model structure in such way that accurate needed information about positions of structures, features, points and dimensions can be easily extracted without having to pay physical visits to site to obtain measurement of the various components of structures. In this project, the data acquisition system used is the terrestrial laser scanner, High Definition Surveying (HDS) equipment; the methodology employed is similar to Close Range Photogrammetry (CRP). CRP is a budding technique or field used for data acquisition in Geomatics. It is a subset of the general photogrammetry; it is often loosely tagged terrestrial photogrammetry. The terrestrial laser scanning technology is a data acquisition system similar to CRP in terms of deigning the positioning of instrument and targets, calibration, ground control point, speed of data acquisition, data processing (interior, relative and absolute orientation) and the accuracy obtainable. The aim of this project was to generate the three-dimensional model of structures in the Faculty of Engineering, University of Lagos using High Definition Surveying, the Leica Scan Station 2 HDS equipment was used along with Cyclone software for data acquisition and processing. The result was a 3D view (of point clouds) of the structure that was studied, from which features were measured from the model generated and compared with physical measurement on site. The technology of the laser scanner proved to be quite useful and reliable in generating three dimensional models without compromising accuracy and precision. The generation of the 3D models is the replica of reality of the structures with accurate dimensions and location.
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Links:
http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814
Seven Most tenable applications of AI o Water Resources ManagementMrinmoy Majumder
AI or Artificial Intelligence is a pioneering technique that has enabled the creation of intelligent machines.or smart machines which has the power to self adapt based on the situation presented to it. It requires situations whose response is known and based on this training data set it learns the problems which it has to solve when it is ready. Due to the alarming success with AI in robotics, electronics etc fields the same technique is now used to solve the problems of water resource management.. This ppt shows seven most notable use of AI in water resources-based problems where satisfactory improvement has encouraged further application of the technique.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
08.09.19
Invited Lecture to the Green IT Workshop
Canada-California Strategic Innovation Partnership
Title: Toward Greener Cyberinfrastructure
Palo Alto, CA
Energy efficient clustering using the AMHC (adoptive multi-hop clustering) t...IJECEIAES
IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive MultiHop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed.
Calit2 - CSE's Living Laboratory for ApplicationsLarry Smarr
08.05.27
UCSD CSE 91 - Perspectives in Computer Science (Spring 2008)
Calit2@UCSD
Title: Calit2 - CSE's Living Laboratory for Applications
La Jolla, CA
In this study, we propose situations where cloud is suitable and fog is more compatible, also define some services according to the cloud and fog architecture. We also provide a comparison of task scheduling algorithms of cloud computing and determine that fog is a light weight network so which is the best suitable algorithm for fog architecture on the basis of some attributes. The implementations of fog computing are challenging in today’s computational era; we define some reasons in which fog computing implementation is difficult.
WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline MonitoringIJECEIAES
One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classical wireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, we present WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
Flood Prediction Model using Artificial Neural NetworkEditor IJCATR
This paper presents a Flood Prediction Model (FPM) to predict flood in rivers using Artificial Neural Network (ANN)
approach. This model predicts river water level from rainfall and present river water level data. Though numbers of factors are
responsible for changes in water level, only two of them are considered. Flood prediction problem is a non-linear problem and to solve
this nonlinear problem, ANN approach is used. Multi Linear Perceptron (MLP) based ANN’s Feed Forward (FF) and Back
Propagation (BP) algorithm is used to predict flood. Statistical analysis shows that data fit well in the model. We present our
simulation results for the predicted water level compared to the actual water level. Results show that our model successfully predicts
the flood water level 24 hours ahead of time.
Fredrick Ishengoma - A Novel Design of IEEE 802.15.4 and Solar Based Autonomo...Fredrick Ishengoma
The recently advancement in Wireless Sensor Network (WSN) technology has brought new distributed sensing applications such as water quality monitoring. With sensing capabilities and using parameters like pH, conductivity and temperature, the quality of water can be known. This paper proposes a novel design based on IEEE 802.15.4 (Zig-Bee protocol) and solar energy called Autonomous Water QualityMonitoring Prototype (AWQMP). The prototype is designed to use ECHERP routing protocol and Adruino Mega 2560, an open-source electronic prototyping platform for data acquisition. AWQMP is expected to give real time data acquirement and to reduce the cost of manual water quality monitoring due to its autonomous characteristic. Moreover, the proposed prototype will help to study the behavior of aquatic animals in deployed water bodies.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
Landslide early warning systems: a perspective from the internet of things IJECEIAES
Populations located in the vicinity of slopes and soils derived from volcanic ash are constantly at risk due to the possibility of landslides. Such is the case of the city of Manizales, Colombia, which, due to its geomorphological characteristics, has experienced a significant number of landslides that have caused human and economic losses. The Internet of things (IoT) has allowed important technological advances for monitoring, thanks to the low cost and wide coverage of IoT-based systems. Slope monitoring and the development of landslide early warning systems (EWS) have been positively impacted by IoT developments, which shows a relationship. The objective of this article is to review, from the scientific production, the relationship between IoT and EWS. For this purpose, a fragmenting-deriving-combining methodology is applied to focus on a research trends analysis of the subject, from macroareas such as IoT and EWS to micro areas such as EWS by IoT-based landslides. Finally, the analysis concluded that the conceptual models of IoT and EWS for landslides have some correspondence in some of their layers.
An advanced ensemble load balancing approach for fog computing applicationsIJECEIAES
Fog computing has emerged as a viable concept for expanding the capabilities of cloud computing to the periphery of the network allowing for efficient data processing and analysis from internet of things (IoT) devices. Load balancing is essential in fog computing because it ensures optimal resource utilization and performance among distributed fog nodes. This paper proposed an ensemble-based load-balancing approach for fog computing environments. An advanced ensemble load balancing approach (AELBA) uses real-time monitoring and analysis of fog node metrics, such as resource utilization, network congestion, and service response times, to facilitate effective load distribution. Based on the ensemble's collective decision-making, these metrics are fed into a centralized load-balancing controller, which dynamically adjusts the load distribution across fog nodes. Performance of the proposed ensemble load-balancing approach is evaluated and compared it to traditional load-balancing techniques in fog using extensive simulation experiments. The results demonstrate that our ensemble-based approach outperforms individual load-balancing algorithms regarding response time, resource utilization, and scalability. It adapts to dynamic fog environments, providing efficient load balancing even under varying workload conditions.
Hand LightWeightNet: an optimized hand pose estimation for interactive mobile...IJECEIAES
In this paper, a hand pose estimation method is introduced that combines MobileNetV3 and CrossInfoNet into a single pipeline. The proposed approach is tailored for mobile phone processors through optimizations, modifications, and enhancements made to both architectures, resulting in a lightweight solution. MobileNetV3 provides the bottleneck for feature extraction and refinements while CrossInfoNet benefits the proposed system through a multitask information sharing mechanism. In the feature extraction stage, we utilized an inverted residual block that achieves a balance between accuracy and efficiency in limited parameters. Additionally, in the feature refinement stage, we incorporated a new best-performing activation function called “activate or not” ACON, which demonstrated stability and superior performance in learning linearly and non-linearly gates of the whole activation area of the network by setting hyperparameters to switch between active and inactive states. As a result, our network operated with 65% reduced parameters, but improved speed by 39% which is suitable for running in a mobile device processor. During experiment, we conducted test evaluation on three hand pose datasets to assess the generalization capacity of our system. On all the tested datasets, the proposed approach demonstrates consistently higher performance while using significantly fewer parameters than existing methods. This indicates that the proposed system has the potential to enable new hand pose estimation applications such as virtual reality, augmented reality and sign language recognition on mobile devices.
Traffic Congestion Detection Using Deep Learningijtsrd
Despite the huge amount of traffic surveillance videos and images have been accumulated in the daily monitoring, deep learning approaches have been underutilized in the application of traffic intelligent management and control. Traffic images, including various illumination, weather conditions, and vast scenarios are considered and preprocessed to set up a proper training dataset. In order to detect traffic congestion, a network structure is proposed based on residual learning to be pre trained and fine tuned. The network is then transferred to the traffic application and retrained with self established training dataset to generate the Traffic Net. The accuracy of Traffic Net to classify congested and uncongested road states reaches 99 for the validation dataset and 95 for the testing dataset. The trained model is stored in cloud storage for easy access for application from anywhere. The proposed Traffic Net can be used by a regional detection of traffic congestion on a large scale surveillance system. The effectiveness and efficiencies are magnificently demonstrated with quick detection in the high accuracy in the case study. The experimental trial could extend its successful application to traffic surveillance system and has potential enhancement for intelligent transport system in future. Anusha C | Dr. J. Bhuvana "Traffic Congestion Detection Using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49401.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49401/traffic-congestion-detection-using-deep-learning/anusha-c
Multi-task learning using non-linear autoregressive models and recurrent neur...IJECEIAES
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short- term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
Total Ionization Cross Sections due to Electron Impact of Ammonia from Thresh...Dr. Amarjeet Singh
In the present paper, we have employed modified Khare-BEB method [Atoms, (2019)] to evaluate total ionization cross sections by the electron impact for ammonia in energy range from the ionization threshold to 10 MeV. The theoretical ionization cross sections have been compared to the available previous theoretical and experimental results. The collision parameters dipole matrix squared M_j^2 and CRP also have been calculated. The present calculations were found in remarkable agreement with the available experimental results.
A Case Study on Small Town Big Player – Enjay IT Solutions Ltd., BhiladDr. Amarjeet Singh
Adequately trained Manpower is a problem that affects the IT industry as a whole, but it is particularly acute for Enjay IT Solution. Enjay's location in a semi-urban or rural area makes it even more difficult to find a talented employee with the right skills. As the competition for skilled workers grows, it becomes more difficult to attract and keep those workers who have the requisite training and experience.
Effect of Biopesticide from the Stems of Gossypium Arboreum on Pink Bollworm ...Dr. Amarjeet Singh
Pink bollworm and Lepidoptera development quickly in numbers which is a typical animal group that produces around 100 youthful ones inside certain days or weeks. This assault influences the harvests broadly in the tropical and sub-tropical temperature areas. Thus, to keep up with the yield of harvests the vermin ought to be kept away by utilizing pesticides. The unnecessary measure of the purpose of pesticides influences the dirt, land, and as well as human well-being, and contaminates the climate. Thus, an ozone-accommodating biopesticide is extracted from the stems of the Gossypium arboreum. Thus, the extraction of biopesticide from the stems of Gossypium arboreum demonstrated that the quantity of pink bollworm and Lepidoptera is diminished step by step in the wake of showering the arrangement on the impacted region of the plant because of the presence of the gossypol.
Artificial Intelligence Techniques in E-Commerce: The Possibility of Exploiti...Dr. Amarjeet Singh
E-Commerce has transformed business as we know over the past few decades. The rapid increasing use of the Internet and the strong purchasing power in Saudi Arabia have had a strong impact on the evolution of E-Commerce in the country. Saudi Arabia is yet another country that will release artificial intelligence power to fuel its growth in the economic world. Recently, artificial intelligence (AI) applications that can facilitate e-commerce processes have been widely used. The impact of using artificial intelligence (AI) concepts and techniques on the efficiency of e-commerce, particularly has been overlooked by many prior studies. In this paper, a literature review was conducted to explore and investigate possible applications of AI in E-Commerce that can help Saudi Arabian businesses.
Factors Influencing Ownership Pattern and its Impact on Corporate Performance...Dr. Amarjeet Singh
This study on factors influencing Ownership pattern and its impact on corporate performance has used five industries data viz Automobile industry, IT industry, Banking industry, Oil & Gas industry and pharmaceutical industry for five years from 2017 to 2021. First the factors influencing ownership pattern was identified and later its impact on corporate performance was analysed. Multiple Regression, ANOVA and Correlation was used in SPSS 28. Percentage of independent directors on the board and size of the company has significant impact on Indian Promotor holding and non-institutional ownership has significant impact on corporate performance.
An Analytical Study on Ratios Influencing Profitability of Selected Indian Au...Dr. Amarjeet Singh
Every country with a well-developed transportation network has a well-developed economy. The automobile industry is a critical engine of the nation's economic development. The automobile industry has significant backward and forward links with every area of the economy, as well as a strong and progressive multiplier impact. The automotive industry and the auto component industry are both included in the vehicle industry. It includes passenger waggons, light, medium, and heavy commercial vehicles, as well as multi-utility vehicles such as jeeps, three-wheelers, military vehicles, motorcycles, tractors, and auto-components such as engine parts, batteries, drive transmission parts, electrical, suspension and chassis parts, and body and other parts. In the last several years, India's automobile sector has seen incredible growth in sales, production, innovation, and exports. India's car industry has emerged as one of the best in the world, and the auto-ancillary sector is poised to assist the vehicle sector's expansion. Vehicle manufacturers and auto-parts manufacturers account for a significant component of global motorised manufacturing. Vehicle manufacturers from across the world are keeping a close eye on the Indian auto sector in order to assess future demand and establish India as a global manufacturing base. The current research focuses on three automotive behemoths: TATA Motors, MRF, and Mahindra & Mahindra.
A Study on Factors Influencing the Financial Performance Analysis Selected Pr...Dr. Amarjeet Singh
The growth of a country's banking sector has a significant impact on its economic development. The banking sector plays a critical role in determining a country's economic future. A well-planned, structured, efficient, and viable banking system is an essential component of an economy's economic and social infrastructure. In modern society, a strong banking system is required because it meets the financial needs of the modern society. In a country's economy, the banking system plays a crucial role. Because it connects surplus and deficit economic agents, the bank is the most important financial intermediary in the economy. The banking system is regarded as the economy's lifeline. It meets the financial needs of commerce, industry, and agriculture. As a result, the country's development and the banking system are intertwined. They are critical in the mobilisation of savings and the distribution of credit to various sectors of the economy. India's private sector banks play a critical role in the country's economic development. So The financial performance of private sector banks must be evaluated carefully.
An Empirical Analysis of Financial Performance of Selected Oil Exploration an...Dr. Amarjeet Singh
After the United States, China, and Japan, India was the world's fourth biggest consumer of oil and petroleum products. The nation is significantly reliant on crude oil imports, the majority of which come from the Middle East. The Indian oil and gas business is one of the country's six main sectors, with important forward links to the rest of the economy. More than two-thirds of the country's overall primary energy demands are met by the oil and gas industry. The industry has played a key role in placing India on the global map. India is now the world's sixth biggest crude oil user and ninth largest crude oil importer. In addition, the country's portion of the worldwide refining market is growing. India's refining industry is now the world's sixth biggest. With plans for Reliance Petroleum Limited to commission another refinery with a capacity of 29 MTPA next 16 to its 33 MTPA refinery in Jamnagar, Gujarat, this position is projected to be enhanced. As a consequence, the Reliance refinery would be the biggest single-site refinery in the world. Based on secondary data gathered from CMIE, the current research examines the ratios influencing the profitability of selected oil exploration and production businesses in India during a 10-year period.
Since 1991, thanks to economic policy liberalization, the Indian economy has entered an era in which Indian businesses can no longer disregard global markets. Prior to the 1990s, the prices of a variety of commodities, metals, and other assets were carefully regulated. Others, which were not rolled, were primarily dependant on regulated input costs. As a result, there was no uncertainty and, as a result, no price fluctuations. However, in 1991, when the process of deregulation began, the prices of most items were deregulated. It has also resulted in the exchange being partially deregulated, easing trade restrictions, lowering interest rates, and making significant advancements in foreign institutional investors' access to the capital markets, as well as establishing market-based government securities pricing, among other things. Furthermore, portfolio and securities price volatility and instability were influenced by market-determined exchange rates and interest rates. As a result, hedging strategies employing a variety of derivatives were exposed to a variety of risks. The Indian capital market will be examined in this study, with a focus on derivatives.
Theoretical Estimation of CO2 Compression and Transport Costs for an hypothet...Dr. Amarjeet Singh
SEI S.p.a. presented a project to build a 1320 MW coal-fired power plant in Saline Joniche, on the Southern tip of Calabria Region, Italy, in 2008. A gross early evaluation about the possibility to add CCS (CO2 Capture & Storage) was performed too. The project generated widespread opposition among environmental associations, citizens and local institutions in that period, against the coal use to produce energy, as a consequence of its GHG clima-alterating impact. Moreover the CCS (also named Carbon Capture & Storage or more recently CCUS: Carbon Capture-Usage-Storage) technology was at that time still an unknown and “mysterious” solution for the GHG avoiding to the atmosphere. The present study concerns the sizing of the compression and transportation system of the CCS section, included in the project presented at the time by SEI Spa; the sizing of the compression station and the pipeline connecting the plant to the possible Fosca01 offshore injection site previously studied as a possible storage solution, as part of a coarse screening of CO2 storage sites in the Calabria Region. This study takes into account the costs of construction, operation and maintenance (O&M) of both the compression plant and the sound pipeline, considering the gross static storage capacity of the Fosca01 reservoir as a whole as previously evaluated.
Analytical Mechanics of Magnetic Particles Suspended in Magnetorheological FluidDr. Amarjeet Singh
In this paper, the behavior of MR particles has been systematically investigated within the scope of analytical mechanics. . A magnetorheological fluid belongs to a class of smart materials. In magnetorheological fluids, the motion of magnetic particles is controlled by the action of internal and external forces. This paper presents analytical mechanics for the interaction of system of particles in MR fluid. In this paper, basic principles of Analytical Mechanics are utilized for the construction of equations.
Techno-Economic Aspects of Solid Food Wastes into Bio-ManureDr. Amarjeet Singh
Solid waste is health hazard and cause damage to the environment due to improper handling. Solid waste comprises of Industrial Waste (IW), Hazardous Waste (HW), Municipal Solid Waste (MSW), Electronic waste (E-waste), Bio-Medical Waste (BMW) which depend on their supply & characteristics. Food waste or Bio-waste composting and its role in sustainable development is explained in food waste is a growing area of concern with many costs to our community in terms of waste collection, disposal and greenhouse gases. When rotting food ends up in landfill it turns into methane, a greenhouse gas that is particularly damaging to the environment. Composting is biochemical process in which organic materials are biologically degraded, resulting in the production of organic by products and energy in the form of heat. Heat is trapped within the composting mass, leading to the phenomenon of self-heating. This overall process provide us Bio-Manure.
Crypto-Currencies: Can Investors Rely on them as Investment Avenue?Dr. Amarjeet Singh
The purpose of this study is to examine investors’ perceptions about investing in crypto-currencies. We think that investors trust in crypto-currencies is largely driven by crypto-currency comprehension, trust in government, and transaction speed. This is the first study to examine crypto-currencies from the investor’s perspective. Following that, we discover important antecedents of crypto-currency confidence. Second, we look at the government's role in crypto-currencies. The importance of this study is: first, crypto-currencies have the potential to disrupt the current economic system as the debate is all about impact of decentralization of transactions; thus, further research into how it affects investors trust is essential; and second, access to crypto-currencies. Finally, if Fin-Tech companies or banks want to enter the bitcoin industry may not attract huge advertising costs as well as marketing to soothe clients' concerns about investing in various digital currencies The research sheds light on indecisiveness in the context of marketing aspects adopted by demonstrating investors are aware about the crypto.
Awareness of Disaster Risk Reduction (DRR) among Student of the Catanduanes S...Dr. Amarjeet Singh
The Island Province of Catanduanes is prone to all types of natural hazards that includes torrential and heavy rains, strong winds and surge, flooding and landslide or slope failures as a result of its geographical location and topography. RA 10121 mandates local DRRM bodies to “encourage community, specifically the youth, participation in disaster risk reduction and management activities, such as organizing quick response groups, particularly in identified disaster-prone areas, as well as the inclusion of disaster risk reduction and management programs as part of youth programs and projects. The study aims to determine the awareness to disaster of the student of the Catanduanes State University. The disaster-based questionnaire was prepared and distributed among 636 students selected randomly from different Colleges and Laboratory Schools in the University
The Catanduanes State University students understood some disaster-related concepts and ideas, but uncertain on issues on preparedness, adaptation, and awareness on the risks inflicted by these natural hazards. Low perception on disaster risks are evidently observed among students. The responses of the students could be based on the efficiency and impact of the integration of DRR education in the senior high school curriculum. Specifically, integration of the concepts about the hazards, hazard maps, disaster preparedness, awareness, mitigation, prevention, adaptation, and resiliency in the science curriculum possibly affect the knowledge and understanding of students on DRR. Preparedness drills and other forms of capacity building must be done to improve awareness of the student towards DRRM.
The study further recommends that teachers and instructor must also be capacitated in handling disaster as they are the prime movers in the implementation of the DRRM in education. Preparedness drills and other forms of capacity building must be done to improve awareness of the student towards DRRM. Core subjects in Earth Sciences must be reinforced with geologic hazards. Learning competencies must also be focused on hazard identification and mapping, and coping with different geologic disaster.
The 1857 war was a watershed moment in the history of the Indian subcontinent. The battle has sparked academic debate among historians and sociologists all around the world. Despite the fact that it has been more than 150 years, this battle continues to pique the interest of historians. The war's causes and events that occurred throughout the conflict, persons who backed the British and anti-British fighters, and the results and ramifications, are all aspects of this conflict. In terms of outcomes, many academics believe that the war was a failure for those who started it. It is often assumed that the Indians who battled the British in this conflict were unable to achieve their goals. Many gains accrued to Indians as a result of the conflict, but these achievements are overshadowed by the dispute over the war's failure. This research effort focuses on the war's achievements for India, and the significance of those achievements.
Haryana's Honour Killings: A Social and Legal Point of ViewDr. Amarjeet Singh
Life is unpredictably unpredictable. Nobody knows what will happen in the next minute of their lives. In this circumstance, every human being has the right and desire to conduct their lives according to their own desires. No one should be forced to live a life solely for the benefit and reputation of others. Honour killing is defined as the assassination of a person, whether male or female, who refuses to accept the family's arranged marriage or decides to move her or his marital life according to her or his wishes solely because it jeopardizes the family's honour. The family's supreme authority looks after the family's name but neglects to consider the love and affection shared among family members. I have discussed honour killing in India in my research work. This sort of murder occurs as a result of particular triggers, which are also examined in relation to the role of the law in honour killing. No one can be released free if they break the law, and in this case, it is a felony that violates various regulations designed to safeguard citizens. This crime is similar to many others, but it is distinct enough to be differentiated in the report. When the husband is of low social standing, it lowers the position and caste of the female family, prompting the male family members to murder the girl. But they forget that the girl is their kid and that while rank may be attained, a girl's life can never be replaced, and that caste is less valuable than the girl's life and love spent with them.
Optimization of Digital-Based MSME E-Commerce: Challenges and Opportunities i...Dr. Amarjeet Singh
The impact caused by the Covid-19 Pandemic on Micro and Small and Medium Enterprises (MSMEs) was so severe and fatal
that not a few went out of business. The heavy burden is borne by MSME actors due to social restrictions imposed by the
government, the declining purchasing power of the people, a product that continues to decline until capital runs out. Plus
inadequate knowledge in carrying out marketing strategies and product innovations are the main trigger for the lack of
enthusiasm for MSME actors as well as bankruptcy. MSME digitalization-based e-commerce is an opportunity and the right
solution in dealing with the obstacles caused by the impact of Covid-19, as well as a challenge for MSME actors to design old
ways in new ways through digital business.
Modal Space Controller for Hydraulically Driven Six Degree of Freedom Paralle...Dr. Amarjeet Singh
This paper presents the Modal space decoupled control for a hydraulically driven parallel mechanism has been presented. The approach is based on singular values decomposition to the properties of joint-space inverse mass matrix, and mapping of the control and feedback variables from the joint space to the decoupling modal space. The method transformed highly coupled six-input six-output dynamics into six independent single-input single-output (SISO) 1 DOF hydraulically driven mechanical systems. The novelty in this method is that the signals including control errors, control outputs and pressure feedbacks are transformed into decoupled modal space and also the proportional gains and dynamic pressure feedback are tuned in modal space. The results indicate that the conventional controller can only attenuate the resonance peaks of the lower eigenfrequencies of six rigid modes properly, and the peaking points of other relative higher eigenfrequencies are over damped, The further results show that it is very effective to design and tune the system in modal space and that the bandwidth increased substantially except surge (x) and sway (y) motions, each degree of freedom can be almost tuned independently and their bandwidths can be increased near to the undamped eigenfrequencies.
It is a known fact that a large number of Steel Industry Expansion projects in India have been delayed due to regulatory clearances, environmental issues and problems pertaining to land acquisition. Also, there are challenges in the tendering phase that affect viability of projects thus delaying implementation, construction phase is beset with over-runs and disputes and last but not the least; provider skills are weak all across the value chain. Given the critical role of Steel Sector in ensuring a sustained growth trajectory for India, it is imperative that we identify the core issues affecting completion of infrastructure projects in India and chalk out initiatives that need to be acted upon in short term as well as long term.
A blockchain is a decentralised database that is shared across computer network nodes. A blockchain acts as a database, storing information in a digital format. The study primarily aims to explore how in the future, block chain technology will alter several areas of the Indian economy. The current study aims to obtain a deeper understanding of blockchain technology's idea and implementation in India, as well as the technology's potential as a disruptive financial technological innovation.
Secondary sources such as reports, journals, papers, and websites were used to compile all the data. Current and relevant information were utilised to help understand the research goals. All the information is rationally organised to fulfil the objectives. The current research focuses on recommendations for enhancing India's Blockchain ecosystem so that it may become one of the best in the world at utilising this new technology.
The Evolution of Non-Banking Financial Companies (NBFCs) in India: Challenges...beulahfernandes8
Role in Financial System
NBFCs are critical in bridging the financial inclusion gap.
They provide specialized financial services that cater to segments often neglected by traditional banks.
Economic Impact
NBFCs contribute significantly to India's GDP.
They support sectors like micro, small, and medium enterprises (MSMEs), housing finance, and personal loans.
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth Capital
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
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Complexity Neural Networks for Estimating Flood Process in Internet-of-Things Empowered Smart City
1. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
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118 This Work is under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Complexity Neural Networks for Estimating Flood Process in Internet-
of-Things Empowered Smart City
Mustafa Ahmed Othman Abo Mhara
Department of Electronics Commerce, Faculty of Economics and Political Science, Bani Walid University, LIBYA
Corresponding Author: mustafaabomhara2018@gmail.com
ABSTRACT
With the advancement of the Internet of Things
(IoT)-based water conservation computerization,
hydrological data is increasingly enriched. Considering
the ability of deep learning on complex features
extraction, we proposed a flood process forecasting model
based on Convolution Neural Network(CNN) with two-
dimension(2D) convolutional operation. At first, we
imported the spatial-temporal rainfall features of the
Xixian basin. Subsequently, extensive experiments were
carried out to determine the optimal hyper parameters of
the proposed CNN flood forecasting model.
Keywords— Flood, Forecasting, Deep Learning, CNN,
Spatial-Temporal Feature, Geographical Feature
I. INTRODUCTION
Flood disasters usually result in a large number
of casualties and property losses. According to statistics,
a 40% loss of the world economy is attributed to the
flood and its secondary disasters, Ruslan, Zain, Adnan
and Samad (2012).The accurate prediction of the flood-
forming process is therefore crucial to public safety and
to the assets of René, Djordjevi, Butler, Mark, Henonin,
Eisum and Madsen (2018). As a result, countries around
the world have invested a lot of manpower and financial
resources to improve the flood forecasting capability of
Cloke and Pappen-Berger (2009).The technology of the
Internet of Things (IoT) and Cyber-Physical Systems
(CPS) have absorbed many research interests recently.
IoT is widely used in flood monitoring, forecasting, and
management. In China, I proposed a basic framework
based on IoT for water conservancy. Over 1,000 people
shared their flood-related experiences with the author.
The author wishes to share his findings with the flood
researchers at the conference. I amended this article on
February 15, 2019, to remove references to ‘smart
watches’ and “smart meters’.
Figure 1: The basic framework of water conservancy information Yang(2009)
Let the IoT technology and help can monitor
hydrological data to prevent flood disasters. Application
of a computer network could reduce the labor cost,
alleviate the time complexity for real-time acquisition,
improve the efficiency for water conservancy
information sharing and processing. Prediction of floods
has been a hot topic since ancient times. However, the
formation of the flooding process is a complex nonlinear
process a variety of factors affects it.
Experts and scholars have contributed a lot of
efforts before to reduce the loss caused by a flood.
Traditional hydrological forecasting model and data-
driven hydrology forecasting model. Sherman et al.
proposed the unit hydrograph theory in 1932 to study the
relationship between rainfall and stream flow. Hu and
Wang (2010), proposed a rain-runoff model - the XAJ
model, which has been widely applied in humid and sub-
humid areas. Beven and Kirkby proposed the TOP
MODEL (Top Graph-based Hydrological MODEL)
MODEL in 1979.
Todini and Ciarapica (2001). It is a method
based on the combination of kinematics and basin
topography, which is widely used in regions without
data. Morris developed an IHDM(Institute of Hydrology
Distributed Model) Beven, Calver, and Morris ( 1987) in
1980.
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The traditional hydrological forecasting model
still absorbs many efforts that analyze the principle of
flood formation. Yangbo Chen et al. proposed a
physically based flood forecasting model named Liuxihe
model in 2011. Charles Luo proposed the coastal fall
flood ensemble estimation(COFFEE) model in 2018 for
real timed flood forecasting for the coastal dominated
watersheds in British Columbia during the fall-winter
season. There are many parameters in these models
which usually need artificial calibration. If the mapping
relationship is studied directly from the data, the
artificial calibration of parameters can be avoided, the
efficiency of prediction can be improved, and the labor
cost can be reduced.
Artificial Neural Network(ANN) has been
widely used in the field of flood prediction because of its
good performance in solving nonlinear problems. Some
experts and scholars have designed a large number of
data-driven flood prediction models. Ji Youn Sung et al.
built flood forecasting models with lead times of 1, 2, and
3 hours respectively by using three-layer ANN. Adnan,
Ruslan, Samad and Zain (2013) introduced Kalman filter
to correct the output of ANN. Tan guanghua and Liu
(2002) introduced the adaptive backward propagation
algorithm into the one-step flood prediction model.
With the brilliant success of deep learning
network in computer vision, natural language processing,
and speech, some scholars introduced some algorithms
and methods rely on deep learning to flood prediction
task. Using dynamic attention mechanism and LSTM
method to establish the model, the model has a high
accuracy for large river basins, but the input of the model
is only rainfall and flow, without considering
geographical factors. The results will change after each
tracing. So it is necessary to run the average multiple
times to get the best results. The current model is not
suitable for complex river bashes. It uses a traditional
machine learning model based on support vector
regression (SVR) and BP neural network.
The traditional model requires a lot of
parameters to be calibrated and the data-driven model
cannot predict the flood pro- ness. In this paper, we
introduced the 2D convolutional operation into the field
of flood forecasting. We fuse spatial-temporal distribution
features of rainfall, geographical features, and trend
features by the ability of Convolutional Neural
Network(CNN) on complex feature extraction. The
numerical results show that the model proposed in this
paper meets the requirements of flood forecasts.
1) We proposed a novel flood process forecasting model
based on CNN which can consider rainfall spatial-
temporal feature, geographical feature and trend feature.
2) A new way of thinking to add more features into flood
forecasting model by basin gridding was proposed in this
paper.
3) We conducted extensive experiments on the proposed
model, including the correlated analysis and the deter-
mining of hyper parameters of the model.
The flood forecasting model by using CNN
based on IoT was demonstrated in section 3. In section 4
extensive experiments were used to determine the better
hyper-parameters of the proposed model and we tested
the result. I will put the specific explanation in the part of
related work. I introduced the study area and data
processing in sections 2 and 3.2.
II. RELATED WORK
CNN has been widely used in the field of
computer vision because of its sparse connection and
parameter sharing. Yann LeCun et al. firstly introduced
the convolutional operation into neural network and
proposed the famous LeNet-5 model in 1998. In the same
year, Google team proposed GoogLeNet Szegedy, Liu,
Jia, Sermanet, Reed, Anguelov, Erhan, Vanhoucke and
Rabinovich (2015) and won the first prize in
ILSVRC2014. Based on these backbone networks, many
excellent models and algorithms have emerged in the
fields of image classification, detection, semantic
segmentation and tracking. Backbone networks can be
used to train computers to recognize objects in images.
For more information, please visit: http://www.com.In
recent years, some researchers began to apply CNN to the
field of time series prediction, such as solar energy
prediction, power load prediction and so on. The reason
for convolutional neural network can also perform well in
time series prediction is that it can extract the implied
repeating patterns from the time series; On the other
hand, convolution operation can automatically extract
features from data without additional feature engineering
and prior knowledge; In addition, in terms of noisy time
series, convolutional neural network can also eliminate
noise in data and extract useful features by constructing
hierarchical features Koprinska, Wu and Wang (2018).
Irena Koprinska et al. proposed an energy time
series prediction model based on CNN with two
convolutional layers and two full connections layers.
Shaojie Bai and his colleagues proposed a Temporal
Convolutional Network (TCN) architecture to address the
problem of information leakage from future through 1D
causal convolution. Qian K, Mohamed A, Claudel's Kun
Qian and Claudel ( 2019) work boost the computational
speed of a physics-based 2-D urban flood prediction
method, governed by the Shallow Water Equation (SWE).
CNN is used to recover the dynamics of the 2D SWE.
Zhao, Pang, Xu, Peng, and Zuo (2020) proposed
to use two CNN networks to forecast floods, SCNN and
LeNet-5. Time series prediction methods based on CNN
all use the 1D convolutional operation to study the trend
of the time series, instead of considering additional
features such as rainfall, topography, vegetation, and soil.
They propose a flood process forecasting model that can
fuse rainfall spatial, temporal feature, geographical
feature, and geographical features. The eight synthetic
hydrographs at each of the upstream points input and I
predict the flood according to the relationship between
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water level and flow. They use point-based array-based and imaged based strategies.
Figure 2: Distribution map of the rainfall and hydrological stations in Xixian Basin where Xixian Hydrological
Station is on the right-most side of the map
Figure 3: A geographic map with digital elevations of different hydrological stations in Xixian Basin. trend feature
III. METHODS
3.1 Study Area and Data analysis
The data used in this paper refers to the Xixian
basin, in Henan province, China. The overall area is
about 10190 km2 and includes 50 rainfall stations and
one hydrological station. In order to collect data from
these sensors, an IoT-based platform was established
shown in Fig.4. We introduced the 2D convolutional
operation to the filed of flood process forecasting.
Figure4: The IoT system of Xixian basin
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Figure5: Rainfall data processing flowchart
The data recorded from January 1, 2010 to
September 7, 2018 were used in this paper. Rainfall
stations in different regions of the IoT-based platform
record data only if it rains in that region. We designed a
rainfall data processing flow to integrate data from all
rainfall stations. We estimated the missing rainfall data
by using IDW(Inverse Distance Weighting) method
Suhaila, Sayang and Jemain (2008) which is the most
commonly used method in flood forecasting. The value
of p usually ranges from 1.0 to 6.0, which is set as 2.0 in
the paper.
Figure6: Stream flow data processing flowchart
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Figure7: Processed data of Xixian basin
The missing stream flow data is filled by linear
interpolation Chen, Mei, Xu, Yu and Huang ( 2018)
because of its continuity. The correlation coefficient is a
statistical index. There is a certain time interval between
the stream flow process and the rainfall process due to
the influence of confluence process. The confluence
time varies in different basin which is influenced by the
area, topography, geology, soil and vegetation of the
basin.
Figure8: Correlation coefficients between rainfall and stream flow
3.2 Construct dataset
The average annual rainfall of 50 rainfall
stations was shown in Fig. 9 which means that rainfall
varies in different regions. Traditional methods obtain
the rainfall of the whole basin by weighted summation
of different rainfall stations, which could lose the spatial
distribution information. Gridding method was used in
this basin to get a relatively accurate spatial distribution
of rainfall. A two-dimensional distribution matrix of
rainfall in one hour with length of 144 and width of 103
can be obtained by gridding the Xixian watershed. The
length of forecast rainfall process needs to fit the length
of the final output. Five models with the output length of
24, 36, 48, 60 and 72 were discussed in this paper. In
addition, topography is also one of the most important
factors affecting flood formation. We downloaded the
SRTM-90m DEM(Digit Elevation Model) data from the
Geospatial Data Cloud.
6. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
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Figure9: The average annual rainfall of 50 rainfall stations
Figure10: Basing ridding
We can get the geographical feature of the
Xixian basin which can combine with the rainfall
spatial-temporal feature. Meanwhile, the previous stream
flow process with the same length of historical rainfall
process in rainfall temporal feature is added to
3.3 Model Design
We designed a novel flood process forecasting
model based on A convolutional network with three
layers was used to extract complicated features from
rainfall spatial-temporal feature and geographical
feature.
Figure11: Rainfall spatial-temporal feature
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Figure12: Processing the DEM data
Figure13: The structure of the flood forecasting model based on CNN
A network with two layers was used to predict
the stream flow of the next T hours. The number of
convolutional kernels and neurons in a fully connected
layer are hyper-parameters that need to be determined by
experiments.
IV. EXPERIMENT AND RESULT
4.1 Network Optimization
The input of convolutional network of the model
proposed in this paper is a multi-channel input composed of
the rainfall spatial-temporal features and geographical
feature. Therefore, the number of convolutional kernels is
designed.
Table 3: The performance statistics of different parameter sets of convolutional layers
EC 500-200 TRAIN
RMSE R2
TEST
RMSE R
A 1512.67 0.227 6086.2 0.418
B 1062.07 0.945 4835.47 0.537
C 8167.09 0.958 4646.72 5 0.55
D 1316.96 0.9328 5287.11 0.494
The purpose of a convolution neural network is
to extract and fuse features from the spatiotemporal
distribution data represented by multi-channel. In this
paper, it reduces the size of the input from the three
dimensions of height, width, and channel. The
convolution layer is used for feature extraction and
fusion, and finally, the abstract spatial-temporal
characteristics of rainfall containing geographic
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information are formed. They set the output of the
network as 72; the optimizer is Adam and the learning
rate is 0.005. I fix the number of neurons of the full
connection layer at 500 and 200.
Figure 14: The RMSE and R2 of different parameter sets of convolutional layers
Table 5: The performance statistics of different parameter sets off unll connection layers.
EC 500-200 TRAIN
RMSE R2
TEST
RMSE R
E 560.68 0.713 1095.88 -0.0478
F 6205.89 0.968 4596 0.578
G 8167.09 0.958 1646.72 -4. 0.55
Figure 15: The RMSE and R2 of different parameter sets of full connection layers
In order to further improve the performance of
the proposed model, we poola variety of ideas from the
past work such as BN(Batch Normalization) Ioffe and
Szegedy (2015b), Dropout Krizhevsky, Sutskever and
Hinton (2012b) and L1, L2 regularization Huiand
Hastie(2005). We carefully added these methods to the
network and generated four models as shown in TABLE
6 and set the output length as 24. The performance
statistics as shown in TABLE 7 and Fig.16. As we
gradually add these methods to the network, the R2
score of training set begins to decline, while the R2 score
of testing set begins to rise, which means that the
generalization ability of the model has been improved.
4.2 Discuss the Results
Through extensive experiments, we finally
determined the relatively better parameter combination
of the hyper-Parameters of the model proposed in this
paper. In order to study the influence of lead time on
model performance, the length of the output was set as
24, 36, 48, 60, 72 respectively. The performance
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statistics as shown in TABEL 8 and Figure 17 show that
the performance of the model decreases gradually as the
output length increases. o further verify the effect of the
model, 10 historical flood processes from 2010 to 2018
were used in this paper
Figure 16: The RMSE and R2 of four models with different methods
To verify the accuracy of the model in
predicting the flood peak and arrival occasion as shown
in Figure 18. The number of convolution kernels is
designed to decrease layer by layer, and the input size is
reduced from the three dimensions of length, width and
channel. The convolution layer is used for feature
extraction and fusion, and finally the abstract spatial-
temporal characteristics of rainfall containing
geographic information are formed. The statistics are
represented by multi-channel. The permissible errors of
flood peak is 20% of the measured flood peak flow.
Figure 17: The RMSE and R2 of 5 models with different output length
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The accuracy of flood peak and arrival occasion
can be shown in TABLE 9. When the lead time is 24
hours, the accuracy of flood peak and arrival occasion is
90% and 100%; When the lead time was 36 hours, the
accuracy varied by 80% and 100%. When the lead time
was longer than 48 hours, the flood peak accuracy
dropped to less than 70%. Therefore, the model
proposed in this paper can accurately predict the flood
process in the next 24 or 36 hours.
Figure 18: The performance of different leadtime models in 10 historical flood processes
4.3 Discuss the Errors
The errors of the model proposed in this paper
come from three aspects. In Fig. 19, a small amount of
rainfall caused a large-scale streamflow change, while
some large- scale rainfall processes didn't. This
phenomenon may be due to the sensor damage, data
transmission lost or human factors such as reservoir
storage and drainage.
V. CONCLUSION
With nearly 10 years’ historical data of Xixian
basin collected by IoT, this paper proposed a flood
process fore- casting model based on CNN, which can
accurately predict the peak of flood and arrival occasion
by comprehensively considering the rainfall spatial-
temporal features, geographical feature and trend
feature. The rainfall spatial-temporal feature was
obtained by gridding the basin to obtain the spatial
distribution matrix of rainfall, and then stacking the
spatial distribution matrix of rainfall in different time
periods. The DEM data of the basin is splicing into the
spatial- temporal features of rainfall as a channel after
the average pooling operation. Then, the historical
stream flow process of the basin as the trend feature
combined with the complicated features extract by CNN;
Finally, a full connection network with 2 layers was used
to predict stream flow in multiple periods in the future.
In addition, a large number of experiments are designed
to determine the optimal model parameters. Through the
verification of 10 historical flood processes, it is
ultimately proved that the model proposed in this paper
meets the requirements of flood prediction. In this paper,
CNN is introduced into the field of flood forecasting,
proposed a new way to consider a variety of factors. In
our future work, more features such as evaporation,
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vegetation, soil and other factors will be introduced to
further improve the performance of flood forecasting
model. In addition, we will introduce edge computing
Liu, Chen, Pei, Maharjan and Zhang (2020); Gao, Huang
and Duan (2020a) and nodal caching strategies Chen,
Wang, Qiu, Atiquzzaman and Wu (2020b) into the water
conservation system to speed up the flooding monitoring
and management.
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12. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
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www.ijemr.net https://doi.org/10.31033/ijemr.10.6.16
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