Rainfall Disaggregation modeling using stochastic model, Valencia-Schaake, Lane alongwith application of Artificial Neural Network to Disaggregate higher order scale time series to lower time scale series.
~Shashank Singh~
High dimensional Data Visualization using Star Coordinates on Three Dimensionsinscit2006
This paper enhances the existing 2D star coordinates technique for visualizing high dimensional data and extends it to 3 dimensions. The authors introduce new parameters to improve the visualization and avoid overlapping points. Data points are represented as vectors on 3D axes based on their attribute values. Overlapping points can be distinguished using wireframes or varying opacity. The technique is demonstrated on the iris dataset.
Bayesian assimilation of rainfall sensors with fundamentally different integr...Andreas Scheidegger
Presents "CAIRS", a generic Bayesian method to assimilate signals from traditional and novel rain sensors. CAIRS is available for free as julia package: https://github.com/scheidan/CAIRS.jl
This document evaluates using unsupervised linear unmixing of multi-date hyperspectral imagery to estimate crop yields. Vertex component analysis is used to extract spectral endmembers from the imagery. Crop abundance maps derived from linear unmixing show strong correlations with actual crop yield data, and fusing results from images on different dates improves the correlation further.
1. The document discusses number systems, specifically exploring their use in physical and computational sciences. It focuses on Fibonacci number space and natural events.
2. In Fibonacci number space, energy is defined as a ratio of energy (E) to a base energy (EB). This definition is shown to correlate to the natural logarithm base.
3. Examples of natural phenomena are provided that comply with the number one definition, including the fine structure constant and Planck's constant. Further definitions and relationships involving dimensionless energies and energies at different spatial locations are also given.
Identifying Land Patterns from Satellite Images using Deep LearningSoumyadeep Debnath
▫️ Research Domain :
Machine Learning (ML), Deep Learning (DL) and Convolutional Neural Network (CNN).
▫️ Conference Details :
International Conference on the Networked Digital Earth (ICNDE 2018) at Indian Institute of Technology Kharagpur (IITkgp), India during March 7 - 9, 2018.
https://cse.iitkgp.ac.in/conf/NSDE/sds/ICNDE2018/
▫️ Presentation Details :
Presented the conference poster at ICNDE 2018 in front of Prof. Ravi Sundaram [Northeastern University, Boston, USA], Organizing Chair and Dr. Anil Vullikanti [Virginia Tech, USA], Invited Chair.
This document provides instructions for calculating vegetation indices from Landsat 5 TM and Landsat 7 ETM+ data using ArcGIS. It describes a multi-step process to: 1) reclassify Landsat digital number data to exclude null values, 2) convert Landsat 5 TM data to the Landsat 7 ETM+ format, 3) calculate radiance values, 4) calculate reflectance values using sun elevation angles and earth-sun distances, and 5) enforce positive reflectance values by setting negatives to zero. This allows vegetation indices to be accurately calculated from the reflectance data.
Partial Binomial Distribution method for Generation capacity outage using Spr...vivatechijri
This document summarizes a method for calculating generation capacity outage using the partial binomial distribution method and spreadsheets. It presents a case study of a power system with 2 generators of 3 MW each with a forced outage rate (FOR) of 0.02, and 1 generator of 5 MW with a FOR of 0.03. Spreadsheets are used to calculate the outage probability for various outage levels (0 MW, 3 MW, etc.) considering different combinations of generator failures. The results are presented in a table showing the outage level and corresponding probability to determine the generation capacity reliability of the system. This method allows easy and error-free calculation of reliability compared to traditional manual methods.
Sharing the experience and results of using georeferenced 2010 Census data in Mexico and EO to train algorithms in order to detect urban growth and generate useful information for estimating population for non-census years.
High dimensional Data Visualization using Star Coordinates on Three Dimensionsinscit2006
This paper enhances the existing 2D star coordinates technique for visualizing high dimensional data and extends it to 3 dimensions. The authors introduce new parameters to improve the visualization and avoid overlapping points. Data points are represented as vectors on 3D axes based on their attribute values. Overlapping points can be distinguished using wireframes or varying opacity. The technique is demonstrated on the iris dataset.
Bayesian assimilation of rainfall sensors with fundamentally different integr...Andreas Scheidegger
Presents "CAIRS", a generic Bayesian method to assimilate signals from traditional and novel rain sensors. CAIRS is available for free as julia package: https://github.com/scheidan/CAIRS.jl
This document evaluates using unsupervised linear unmixing of multi-date hyperspectral imagery to estimate crop yields. Vertex component analysis is used to extract spectral endmembers from the imagery. Crop abundance maps derived from linear unmixing show strong correlations with actual crop yield data, and fusing results from images on different dates improves the correlation further.
1. The document discusses number systems, specifically exploring their use in physical and computational sciences. It focuses on Fibonacci number space and natural events.
2. In Fibonacci number space, energy is defined as a ratio of energy (E) to a base energy (EB). This definition is shown to correlate to the natural logarithm base.
3. Examples of natural phenomena are provided that comply with the number one definition, including the fine structure constant and Planck's constant. Further definitions and relationships involving dimensionless energies and energies at different spatial locations are also given.
Identifying Land Patterns from Satellite Images using Deep LearningSoumyadeep Debnath
▫️ Research Domain :
Machine Learning (ML), Deep Learning (DL) and Convolutional Neural Network (CNN).
▫️ Conference Details :
International Conference on the Networked Digital Earth (ICNDE 2018) at Indian Institute of Technology Kharagpur (IITkgp), India during March 7 - 9, 2018.
https://cse.iitkgp.ac.in/conf/NSDE/sds/ICNDE2018/
▫️ Presentation Details :
Presented the conference poster at ICNDE 2018 in front of Prof. Ravi Sundaram [Northeastern University, Boston, USA], Organizing Chair and Dr. Anil Vullikanti [Virginia Tech, USA], Invited Chair.
This document provides instructions for calculating vegetation indices from Landsat 5 TM and Landsat 7 ETM+ data using ArcGIS. It describes a multi-step process to: 1) reclassify Landsat digital number data to exclude null values, 2) convert Landsat 5 TM data to the Landsat 7 ETM+ format, 3) calculate radiance values, 4) calculate reflectance values using sun elevation angles and earth-sun distances, and 5) enforce positive reflectance values by setting negatives to zero. This allows vegetation indices to be accurately calculated from the reflectance data.
Partial Binomial Distribution method for Generation capacity outage using Spr...vivatechijri
This document summarizes a method for calculating generation capacity outage using the partial binomial distribution method and spreadsheets. It presents a case study of a power system with 2 generators of 3 MW each with a forced outage rate (FOR) of 0.02, and 1 generator of 5 MW with a FOR of 0.03. Spreadsheets are used to calculate the outage probability for various outage levels (0 MW, 3 MW, etc.) considering different combinations of generator failures. The results are presented in a table showing the outage level and corresponding probability to determine the generation capacity reliability of the system. This method allows easy and error-free calculation of reliability compared to traditional manual methods.
Sharing the experience and results of using georeferenced 2010 Census data in Mexico and EO to train algorithms in order to detect urban growth and generate useful information for estimating population for non-census years.
Presentation on Salt Lake City Solar Energy Modeling project done in partnership with Utah Clean Energy and the Automated Geographic Reference Center (done in the style of Ignite lightning talks, with a bit of cheating).
We use the Georeferenced results of the 2010 Census in Mexico to train machine learning algorithms to detect growth in cities and contribute new information to estimate the total population.
This document lists 53 commonly used functions in Microsoft Excel 2007. It provides the function name, syntax, and purpose for each function. The functions can be used to perform calculations on cell ranges like summation, average, minimum and maximum. Other functions help extract parts of text, format dates, round numbers, lookup values, and perform financial calculations. Overall, the functions listed provide a variety of tools for manipulating and analyzing data in Excel worksheets.
The document discusses number systems used in the physical and computational sciences and explores natural events in Fibonacci number space. It establishes definitions and relationships for natural logarithms, Planck's constant, gravitational acceleration, energy, and dimensional analysis using Fibonacci numbers. The post aims to clarify the significance of the CH3 molecule in relation to its unique spatial symmetry and position in Fibonacci energy space.
EMPLOYING MULTI CORE ARCHITECTURE TO OPTIMIZE ON PERFORMANCE, FOR APPROACH IN...IAEME Publication
Cloud detection is an important task in meteorological application. Cloud information is especially important for now-casting purposes [1] and as an input for different satellite based estimation of atmospheric and surface parameters [2 -4]. The solar energy is the principal source of energy in the solar system. Clouds have high reflectance and absorption property which is used to distinguish them with land, water or sea area. There is critical demand to develop application, which can calculate the presence of cloud by using the available satellite image processing data, so that prediction of radiated solar energy can be optimised and energy budget can be predicted more easily.
This study conducted a seismic risk assessment for Portugal using probabilistic seismic hazard analysis and developing new exposure and vulnerability models. A probabilistic seismic hazard model was developed considering logic trees for ground motion models, seismic source characterizations, and other parameters. An exposure model was created using a national building census to characterize Portugal's building stock. Fragility functions were developed for reinforced concrete and masonry structures. Probabilistic loss estimates were calculated at different spatial scales to identify regions in Portugal where risk mitigation measures should be prioritized.
This document describes numerical methods for image registration using nonlinear geometric transformations. It aims to summarize techniques for analyzing sets of images affected by geometric distortions, and developing software to reduce those distortions. Key steps include: using phase correlation to determine pixel movement between images; linear and bilinear interpolation to estimate pixel values at fractional coordinates; and arithmetic averaging to create a mean image from the corrected set. Maps of pixel movements between original and mean images are also generated. The overall goal is to produce a registered, sharper mean image estimating the true average value from the input dataset.
Automated Summarisation of Big Data, useR! 2018 Amy Stringer
This document discusses automated summarization of large datasets from the Catlin Seaview Survey, a global coral reef monitoring effort. It collects images of reefs automatically during surveys and annotates them using machine learning. The author aims to efficiently summarize the big data using dynamic RMarkdown reports connected to a MySQL database. This will allow non-experts to explore trends in the data through interactive visualizations and maps.
Math 390 - Machine Learning Techniques PresentationDarragh Punch
This document discusses various machine learning techniques for modeling solar radiation, including artificial neural networks (ANNs), support vector machines (SVMs), radial basis functions (RBFs), support vector regression (SVR), Gaussian processes (GP), and numerical weather prediction (NWP). ANNs can predict optimal photovoltaic system layouts and "learn" from examples. SVMs using RBF kernels are more accurate than other models for solar radiation forecasting. SVR provides better representations than multi-class SVMs. GP is the best predictor of solar irradiance. And NWP samples current weather to predict future conditions up to 6 hours ahead, relevant for long-term solar farm planning.
This document summarizes a study that analyzed the damage scenarios for reinforced concrete precast industrial structures in Tuscany, Italy due to earthquakes. The study generated a population of building models based on inventory data and fragility curves. Nonlinear analyses were performed under earthquake ground motions. Limit states like yielding and collapse were defined. The results showed that accounting for both flexural and connection failures provided more accurate fragility curves compared to flexural failures alone. Connection failures were highly dependent on the assumed friction coefficient. Finally, probabilistic collapse maps for a Mw 6.5 scenario earthquake in Tuscany were presented.
This document summarizes Stephen Quandzie's MSc thesis on the potential to increase agricultural water productivity in Ghana's Black Volta Basin. The research questions whether agricultural water productivity can be increased, if increased dry season agriculture would help reduce poverty, and which agricultural water management intervention has the potential to improve livelihoods sustainably. The methodology involves using the CROPWAT and CLIMWAT models to analyze crop water consumption factors and physical, economic, and livestock water productivity. The results found that small reservoirs, shallow wells, and water pumps with reservoirs over-irrigated crops. Water pumps with reservoirs or river access were identified as having the best potential to enhance agricultural water management and promote off-farm skills development.
Simulating the sensitivity of maize crop propagation to seasonal weather chan...CTA
This study used the CROPWAT model to simulate the effects of temperature changes on maize crop yields in Ibadan, Nigeria. The results showed that rising temperatures had a negative effect on maize yields, though the reduction trends were not definite due to variability in temperatures. Yield reductions were low under definite interval irrigation and high without irrigation. Definite interval irrigation was determined to be the most effective strategy to minimize yield losses from temperature increases and weather variability.
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.
This document discusses using the CROPWAT and CLIMWAT models to evaluate and plan irrigation for cotton and rice crops in India. It summarizes that India currently produces lower rice yields than countries like China and Brazil from the same amount of land. Increasing yields through improved irrigation practices could increase production and reduce land usage. The document then outlines using the CROPWAT model to analyze reference evapotranspiration, crop water requirements, irrigation requirements, and develop irrigation schedules for cotton and rice in the Kurnool region of India based on climatic and soil data. It concludes that significantly increasing crop yields through using CROPWAT could boost the economy by improving farmer livelihoods and freeing up land.
There are 4 parks near the author's house that use different irrigation systems. DDA Park in Sector 11 uses sprinkler irrigation, which sprays water into the air to water the entire soil surface. DDA Park in Sector 6 uses drip irrigation, which applies water slowly at the base of plants. Rotary irrigation is used in DDA Park in Sector 10 and involves mechanically driven sprinklers that reach distances of up to 100 feet. The center-pivot system used in one park conserves water by using less than surface irrigation and reducing labor costs. Overextraction of groundwater has caused levels to drop by over 5 feet per year in some places.
Presentation on Salt Lake City Solar Energy Modeling project done in partnership with Utah Clean Energy and the Automated Geographic Reference Center (done in the style of Ignite lightning talks, with a bit of cheating).
We use the Georeferenced results of the 2010 Census in Mexico to train machine learning algorithms to detect growth in cities and contribute new information to estimate the total population.
This document lists 53 commonly used functions in Microsoft Excel 2007. It provides the function name, syntax, and purpose for each function. The functions can be used to perform calculations on cell ranges like summation, average, minimum and maximum. Other functions help extract parts of text, format dates, round numbers, lookup values, and perform financial calculations. Overall, the functions listed provide a variety of tools for manipulating and analyzing data in Excel worksheets.
The document discusses number systems used in the physical and computational sciences and explores natural events in Fibonacci number space. It establishes definitions and relationships for natural logarithms, Planck's constant, gravitational acceleration, energy, and dimensional analysis using Fibonacci numbers. The post aims to clarify the significance of the CH3 molecule in relation to its unique spatial symmetry and position in Fibonacci energy space.
EMPLOYING MULTI CORE ARCHITECTURE TO OPTIMIZE ON PERFORMANCE, FOR APPROACH IN...IAEME Publication
Cloud detection is an important task in meteorological application. Cloud information is especially important for now-casting purposes [1] and as an input for different satellite based estimation of atmospheric and surface parameters [2 -4]. The solar energy is the principal source of energy in the solar system. Clouds have high reflectance and absorption property which is used to distinguish them with land, water or sea area. There is critical demand to develop application, which can calculate the presence of cloud by using the available satellite image processing data, so that prediction of radiated solar energy can be optimised and energy budget can be predicted more easily.
This study conducted a seismic risk assessment for Portugal using probabilistic seismic hazard analysis and developing new exposure and vulnerability models. A probabilistic seismic hazard model was developed considering logic trees for ground motion models, seismic source characterizations, and other parameters. An exposure model was created using a national building census to characterize Portugal's building stock. Fragility functions were developed for reinforced concrete and masonry structures. Probabilistic loss estimates were calculated at different spatial scales to identify regions in Portugal where risk mitigation measures should be prioritized.
This document describes numerical methods for image registration using nonlinear geometric transformations. It aims to summarize techniques for analyzing sets of images affected by geometric distortions, and developing software to reduce those distortions. Key steps include: using phase correlation to determine pixel movement between images; linear and bilinear interpolation to estimate pixel values at fractional coordinates; and arithmetic averaging to create a mean image from the corrected set. Maps of pixel movements between original and mean images are also generated. The overall goal is to produce a registered, sharper mean image estimating the true average value from the input dataset.
Automated Summarisation of Big Data, useR! 2018 Amy Stringer
This document discusses automated summarization of large datasets from the Catlin Seaview Survey, a global coral reef monitoring effort. It collects images of reefs automatically during surveys and annotates them using machine learning. The author aims to efficiently summarize the big data using dynamic RMarkdown reports connected to a MySQL database. This will allow non-experts to explore trends in the data through interactive visualizations and maps.
Math 390 - Machine Learning Techniques PresentationDarragh Punch
This document discusses various machine learning techniques for modeling solar radiation, including artificial neural networks (ANNs), support vector machines (SVMs), radial basis functions (RBFs), support vector regression (SVR), Gaussian processes (GP), and numerical weather prediction (NWP). ANNs can predict optimal photovoltaic system layouts and "learn" from examples. SVMs using RBF kernels are more accurate than other models for solar radiation forecasting. SVR provides better representations than multi-class SVMs. GP is the best predictor of solar irradiance. And NWP samples current weather to predict future conditions up to 6 hours ahead, relevant for long-term solar farm planning.
This document summarizes a study that analyzed the damage scenarios for reinforced concrete precast industrial structures in Tuscany, Italy due to earthquakes. The study generated a population of building models based on inventory data and fragility curves. Nonlinear analyses were performed under earthquake ground motions. Limit states like yielding and collapse were defined. The results showed that accounting for both flexural and connection failures provided more accurate fragility curves compared to flexural failures alone. Connection failures were highly dependent on the assumed friction coefficient. Finally, probabilistic collapse maps for a Mw 6.5 scenario earthquake in Tuscany were presented.
This document summarizes Stephen Quandzie's MSc thesis on the potential to increase agricultural water productivity in Ghana's Black Volta Basin. The research questions whether agricultural water productivity can be increased, if increased dry season agriculture would help reduce poverty, and which agricultural water management intervention has the potential to improve livelihoods sustainably. The methodology involves using the CROPWAT and CLIMWAT models to analyze crop water consumption factors and physical, economic, and livestock water productivity. The results found that small reservoirs, shallow wells, and water pumps with reservoirs over-irrigated crops. Water pumps with reservoirs or river access were identified as having the best potential to enhance agricultural water management and promote off-farm skills development.
Simulating the sensitivity of maize crop propagation to seasonal weather chan...CTA
This study used the CROPWAT model to simulate the effects of temperature changes on maize crop yields in Ibadan, Nigeria. The results showed that rising temperatures had a negative effect on maize yields, though the reduction trends were not definite due to variability in temperatures. Yield reductions were low under definite interval irrigation and high without irrigation. Definite interval irrigation was determined to be the most effective strategy to minimize yield losses from temperature increases and weather variability.
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.
This document discusses using the CROPWAT and CLIMWAT models to evaluate and plan irrigation for cotton and rice crops in India. It summarizes that India currently produces lower rice yields than countries like China and Brazil from the same amount of land. Increasing yields through improved irrigation practices could increase production and reduce land usage. The document then outlines using the CROPWAT model to analyze reference evapotranspiration, crop water requirements, irrigation requirements, and develop irrigation schedules for cotton and rice in the Kurnool region of India based on climatic and soil data. It concludes that significantly increasing crop yields through using CROPWAT could boost the economy by improving farmer livelihoods and freeing up land.
There are 4 parks near the author's house that use different irrigation systems. DDA Park in Sector 11 uses sprinkler irrigation, which sprays water into the air to water the entire soil surface. DDA Park in Sector 6 uses drip irrigation, which applies water slowly at the base of plants. Rotary irrigation is used in DDA Park in Sector 10 and involves mechanically driven sprinklers that reach distances of up to 100 feet. The center-pivot system used in one park conserves water by using less than surface irrigation and reducing labor costs. Overextraction of groundwater has caused levels to drop by over 5 feet per year in some places.
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.
This document summarizes four main irrigation methods: surface irrigation (flooding), sprinkler irrigation (applying water under pressure), drip or trickle irrigation (applying water slowly to the soil), and sub-surface irrigation (flooding water underground). Surface irrigation is the most widely used method, covering 90% of irrigated land. Sprinkler irrigation is ideal for scarce water areas. Drip irrigation conserves water, controls weeds, and applies water at a slow rate matching crop needs. Sub-surface irrigation is used where soil and topography allow watering underground.
This document provides an overview of irrigation engineering. It discusses the necessity of irrigation due to factors like insufficient rainfall and uneven distribution. It describes different types of irrigation systems including flow irrigation, lift irrigation, and storage irrigation. It also defines important terms used in irrigation like duty, delta, command area. The document outlines the benefits of irrigation such as increased crop yields and prosperity of farmers. It also notes some ill effects like raising water tables and creating breeding grounds for mosquitoes. Overall, the document provides a broad introduction to key concepts in irrigation engineering.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
Irrigation is the process of transporting water from areas with abundant supply like rivers or reservoirs to drier areas for agricultural and domestic purposes. Ancient Mesopotamians and Egyptians developed early irrigation systems using dams, reservoirs, and canals to supply water to lands away from water sources. Irrigation was invented to allow people living farther from water access to drink, grow crops, and meet other needs, and made obtaining water more reliable and regular. Today, irrigation continues to enable farming in more places and help ensure a secure food supply for more people.
1. Irrigation is the artificial supply of water to crops through methods like surface, sprinkler, and drip irrigation. Surface irrigation involves distributing water over the soil surface by gravity in techniques like basin, border, and furrow irrigation.
2. Sprinkler irrigation applies water similar to rainfall through pipes and sprinklers. Drip irrigation drips water slowly from pipes and emitters directly to plant roots.
3. The suitable irrigation method depends on factors like soil type, crop type, technology, costs and previous experience. Surface irrigation is common on loamy and clay soils while sprinkler and drip are more suitable for sandy soils with low water storage.
Optimal neural network models for wind speed predictionIAEME Publication
The document describes using artificial neural networks for wind speed prediction. Specifically, it analyzes the performance of multilayer perceptron networks and radial basis function networks for wind speed forecasting using real-time data collected from wind farms in Coimbatore, India over one year. The models are trained on 3000 samples and tested on 1000 samples. Performance is evaluated using statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error. Results show that the neural network models improve prediction accuracy compared to other approaches and the optimal model depends on factors like the number of hidden neurons and spread value.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real wind speed data collected over one year from wind farms in Coimbatore, India. The experimental results show that the RBF and MLP models can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real-time wind speed data collected from wind farms in Coimbatore, India over one year. The experimental results show that RBF and MLP networks can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error and mean bias error. The RBF and MLP models are able to handle the non-linear patterns in wind speed data, which conventional models struggle with, increasing prediction precision.
Expert system design for elastic scattering neutrons optical model using bpnnijcsa
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backwardpropagation
neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
This document summarizes the steps to perform colored inversion (CI) on seismic data to obtain relative acoustic impedance values. CI involves: 1) Fitting a function to the log spectrum to model it, 2) Computing the difference between the modeled log spectrum and the seismic spectrum, 3) Converting the difference spectrum to an inversion operator, 4) Convolving the operator with the seismic data to obtain relative impedance values. As a quality control, the output impedance spectrum can be checked against the input log spectrum. The document provides code to implement this CI workflow using open-source Python libraries on a dataset from the Netherlands. CI produces informative relative impedance images to aid seismic interpretation.
Compressive Data Gathering using NACS in Wireless Sensor NetworkIRJET Journal
The document proposes a Neighbor-Aided Compressive Sensing (NACS) scheme for efficient data gathering in wireless sensor networks. NACS exploits both spatial and temporal correlations in sensor data to reduce data transmissions compared to existing compressive sensing models like Kronecker Compressive Sensing (KCS) and Structured Random Matrix (SRM). In NACS, each sensor node sends its raw sensor readings to a uniquely selected nearest neighbor node, which then applies compressive sensing measurements and sends the compressed data to the sink node. Simulation results show NACS achieves better data recovery performance using fewer transmissions than KCS and SRM, improving energy efficiency for data gathering in wireless sensor networks.
Developing digital signal clustering method using local binary pattern histog...IJECEIAES
In this paper we presented a new approach to manipulate a digital signal in order to create a features array, which can be used as a signature to retrieve the signal. Each digital signal is associated with the local binary pattern (LBP) histogram; this histogram will be calculated based on LBP operator, then k-means clustering was used to generate the required features for each digital signal. The proposed method was implemented, tested and the obtained experimental results were analyzed. The results showed the flexibility and accuracy of the proposed method. Althoug different parameters of the digital signal were changed during implementation, the results obtained showed the robustness of the proposed method.
This document summarizes research on using particle swarm optimization to reconstruct microwave images of two-dimensional dielectric scatterers. It formulates the inverse scattering problem as an optimization problem to find the dielectric parameter distribution that minimizes the difference between measured and simulated scattered field data. Numerical results show that a particle swarm optimization approach can accurately reconstruct the shape and dielectric properties of a test cylindrical scatterer, with lower background reconstruction error than a genetic algorithm approach. The research demonstrates that particle swarm optimization is a suitable technique for high-dimensional microwave imaging problems.
A flexible method to create wave file features IJECEIAES
This document presents a flexible method for extracting features from wave files using k-mean clustering. The method calculates the histogram of a wave file and uses it as input data for k-mean clustering. K-mean clustering arranges the histogram data into clusters, and the sums or counts within each cluster are then used as features to represent the original wave file, reducing its size. The method is tested on example wave files and sinusoidal signals. Experimental results show that the proposed k-mean clustering approach extracts consistent features even when signal parameters or sampling frequencies change, unlike statistical feature extraction methods.
ICIS - Power price prediction with neural networksICIS
Neural Networks have received widespread attention for their ability to forecast in complex environments with numerous influences and high volatility. These models learn by identifying patterns and bits of information in the data and use this for projections of the future. In the scope of power market analysis, Neural Networks are seen as a major breakthrough for dealing with renewable generation uncertainty and to reduce the complexity of required modelling assumptions. Sign up for a free trial: www.icis.com/german-spot-price
This document summarizes an algorithm for blind source separation using independent component analysis (ICA). ICA is used to separate mixed images into their original independent components without knowing the mixing process. The proposed algorithm first preprocesses the data through centering and whitening. It then uses an iterative approach to maximize the non-Gaussianity of the independent components, extracting them one by one through deflation. Simulation results on mixtures of 5 images show the algorithm can effectively separate the images, with peak signal-to-noise ratios for the recovered images similar to the originals.
Estimation of clearness index from different meteorological parameters in IRAQIOSR Journals
The aim of this paper is to estimate the mean monthly values of clearness index in five meteorological stations in Iraq (Mosul , Kirkuk , Rutba , Baghdad , Nasiriya) for the period (1970-2000) using different meteorological parameters. Five different models (Linear , Quadratic , Logarithmic , Linear logarithmic , Power) were used to estimate clearness index. The performance of this regression models were evaluated by comparing the calculated clearness index and the measured clearness index . Several statistical tests were used to control the validation and goodness of the regression models in terms of correlation coefficient, coefficient of determination , Mean absolute error and root mean square error . Results showed that Linear model between (KT & n/N) and between (KT & Rainfall) were the best fit in all stations. Quadratic model were the best fit between (KT & cloudiness) , and power model were the best fit between (KT &Evaporation). Linear model and Quadratic model were the best fit between (KT & RH) , while power model , Quadratic model , and Linear model were the best fit between (KT & Tmean).
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
T. Lucas Makinen x Imperial SBI WorkshopLucasMakinen1
1) The document discusses using neural networks to compress cosmological simulations into informative summaries or statistics in order to perform inference on cosmological parameters.
2) It describes using "Information Maximizing Neural Networks" which are trained to maximize the Fisher information of the summaries with respect to the parameters in order to capture the most cosmologically relevant information.
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Stochastic modeling of Rainfall Disaggregation using ANN
1. RAINFALL DISAGGREGATION USING ARTIFICIAL
NEURAL NETWORKS
Shashank Singh, R Subbaiah and H D Rank
College of Agricultural Engineering and
Technology,
Junagadh Agricultural University,
Junagadh 361 001
shashanksinghb4u@gmail.com
2. ARTIFICIAL NEURAL
NETWORKS
• Massively parallel distributed information
processing system resembling biological neural
networks of human brain
• First development in 1943 ( Mcculloch and Pitts)
• Engineering applications : signal processing,
robotics, control, hydrology, geotechnical
engineering to name a few
3. An average human brain has from 4 x 1010 to 1011
neurons. With the possibility of up to 104
interconnections per neuron, that enables 1015
interconnections (between neurons). A neuron is a
specialized cell for receiving, processing and
transmitting information by biochemical means
(neurotransmitters).
Structure of a biological neuron
7. OBJECTIVE
To disaggregate the annual rainfall series into
monthly rainfall series and monthly series to weekly
series using feed forward artificial neural network.
8. Rainfall Disaggregation
Disaggregation models are widely used tools for the
stochastic simulation of hydrologic series. They divide
known higher-level values (e.g. annual) into lower level ones
(e.g. seasonal), which add up to the given higher level. Thus
ability to transform a series from a higher time scale to a
lower one.
9. Mathematically
ε
B
AX
Y
Valencia and Schaake Model (1972, 1973)
1
xx
yx
S
S
A
xy
xx
yx
yy
t
S
S
S
S
BB 1
X is annual flow value and
Y is the column matrix containing the seasonal flow
values
10. RAINFALL DISAGGREGATION USING ARTIFICIAL
NEURAL NETWORKS
Data transformation for input
max
2
.
1
1
.
0
X
Xact
Where,
Xact = Actual values of historical rainfall series, and
Xmax = Maximum value of rainfall in a series
Step I
11. Step II
Division of the input and output data set into two
groups, first is used to train the network and second
set is used to validate the model.
Step III
The following parameters were kept constant for
ANN model during the study,
Momentum Rate = 0.9
Acceleration = 0.9
Permissible testing error = 0.001
The momentum rate keeps changing weight on a
faster, more even path and helps to avoid local
minima. Acceleration affects the size of step taken
through weight space at each training iteration.
12. Step IV
Each successive node receives the information from
all the nodes of the preceding layer as sum of
weighted function of activation function (e.g. Sigmoid
function) used for training the network.
Step V
Transform the outputs as inverse function of
formula used in step I.
Step VI
Calculation of output errors. The difference
between the historical and the ANN generated
value is calculated. Continue epoch till desired
error is met.
13. Step VII
Validation the network using out-of-sample data. If
out-of-sample RMSE,BIC,AIC and coefficient of
skewness is consistent with training RMSE
BIC,AIC and coefficient of skewness the model
appears valid.
Step VIII
If the model is not valid, repeat the experiment
(a) Try different initial values for the weights.
(b) Redesign the ANN
(c) Try a different ANN method
14. N
P
M
RMSE
N
t
t
t
1
2
)
(
Mt = Measured value
Pt = Predicted value
N = Sample size
1. Root Mean Square Error
Error Functions for Evaluating ANN Models
16.
X
X
X
X
M
X
X
M
i
i
3
1
3
2
3
3
4. Coefficient of Skewness
Xi = Historical rainfall series.
series
historical
of
Mean
X
deviation
Standard
X
17. RESULTS
Several network architectures were tried to attain a low value of
RMSE. This was done by trial and error evaluation.
During the trial run different combination of layers and number of
neurons was checked for each about 1,00,000 iterations.
Three layer ANN architecture with one neuron in input layer, 10
neuron in one hidden layer and 4 neuron in output layer, (1-10-4)
was sufficient to disaggregate the rainfall series from annual to
monthly and monthly to weekly.
18. The generated monthly rainfall series are almost congruent
with the historical series (Fig 1). The scatter plot diagrams
between the ANN generated and historical monthly series
clearly showed that the generated values had values closer
to that of the historical values (Fig 2).
19. Table 1 RMSE, AIC, BIC values for ANN (1-10-4) generated
series four months
Season RMSE Skewness AIC BIC
June 13.826 2.253 3.8264 2.8583
July 37.244 3.861 4.2807 3.9366
August 34.306 2.162 4.1983 3.8472
September 6.9674 0.455 2.5993 2.1125
21. june
0
50
100
150
200
250
300
350
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Years
Rainfall
Ann Generated
Historical
Fig. 1 Comparison between historical and generated
disaggregated June rainfall
22. july
0
300
600
900
1200
1 3 5 7 9 11 13 15
year
Rainfall
ANN generated Historical
Fig. 2 Comparison between historical and generated
disaggregated July rainfall