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
ANN Modeling of Monthly and Weekly Behaviour of the Runoff of Kali River Catc...IOSR Journals
Model is a system, by whose operation; the characteristics of other similar systems can be ascertained. Experimental observation made on a model bear a definite relationship with prototype. So, the model analysis or modeling is actually an experimental method of finding solution of complex flow problems like surface water modeling, sub-surface water modeling etc. Many flow situations are not amenable to theoretical analysis. Modeling is a valuable means of obtaining better understanding of particular situation. Inspired by the functioning of the brain and biological nervous system, Artificial Neural Networks (ANNs) has been applied to various hydrological problems in last two decades. In this study, two ANN models using feed forward – back propagation network are developed to correlate a relationship between rainfall and runoff on monthly and weekly basis for Kali river catchment up to Supa dam in Uttara Kannada District of Karnataka State, India. The developed two models are compared and evaluated using standard statistical parameters to know strength and weaknesses. This performance can be further refined by incorporating more input parameters of catchment properties like soil moisture index; land use and land cover details etc.
Use of Evolutionary Polynomial Regression (EPR) for Prediction of Total Sedim...CSCJournals
This study presents the use of Evolutionary Polynomial Regression (EPR) in predicting the total sediment load of ten selected rivers in Malaysia. EPR is a data-driven hybrid technique, based on evolutionary computing. In order to apply the method, the extensive database of the Department of Irrigation and Drainage (DID), Ministry of Natural Resources & Environment, Malaysia was sought, and unrestricted access was granted. The EPR technique produced greatly improved results compared to other previous sediment load methods. A robustness study was performed in order to confirm the generalisation ability of the developed EPR model, and a sensitivity analysis was also conducted to determine the relative importance of model inputs. The performance of the EPR model demonstrates its predictive capability and generalisation ability to solve highly nonlinear problems of river engineering applications, such as sediment.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
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.
This is the presentation for his admission to the third year of his Ph.D.. It talks about the several direction his work had taken and look forward to the conclusion of some task in form of code release and published papers.
ANN Modeling of Monthly and Weekly Behaviour of the Runoff of Kali River Catc...IOSR Journals
Model is a system, by whose operation; the characteristics of other similar systems can be ascertained. Experimental observation made on a model bear a definite relationship with prototype. So, the model analysis or modeling is actually an experimental method of finding solution of complex flow problems like surface water modeling, sub-surface water modeling etc. Many flow situations are not amenable to theoretical analysis. Modeling is a valuable means of obtaining better understanding of particular situation. Inspired by the functioning of the brain and biological nervous system, Artificial Neural Networks (ANNs) has been applied to various hydrological problems in last two decades. In this study, two ANN models using feed forward – back propagation network are developed to correlate a relationship between rainfall and runoff on monthly and weekly basis for Kali river catchment up to Supa dam in Uttara Kannada District of Karnataka State, India. The developed two models are compared and evaluated using standard statistical parameters to know strength and weaknesses. This performance can be further refined by incorporating more input parameters of catchment properties like soil moisture index; land use and land cover details etc.
Use of Evolutionary Polynomial Regression (EPR) for Prediction of Total Sedim...CSCJournals
This study presents the use of Evolutionary Polynomial Regression (EPR) in predicting the total sediment load of ten selected rivers in Malaysia. EPR is a data-driven hybrid technique, based on evolutionary computing. In order to apply the method, the extensive database of the Department of Irrigation and Drainage (DID), Ministry of Natural Resources & Environment, Malaysia was sought, and unrestricted access was granted. The EPR technique produced greatly improved results compared to other previous sediment load methods. A robustness study was performed in order to confirm the generalisation ability of the developed EPR model, and a sensitivity analysis was also conducted to determine the relative importance of model inputs. The performance of the EPR model demonstrates its predictive capability and generalisation ability to solve highly nonlinear problems of river engineering applications, such as sediment.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
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.
This is the presentation for his admission to the third year of his Ph.D.. It talks about the several direction his work had taken and look forward to the conclusion of some task in form of code release and published papers.
The flexibility and versatility of System Dynamics technique in optimization ...Samson Olakunle OJOAWO
Paper presented at the International Conference on Modeling, Optimization, and Computing, ICMOC 2014, N.I. University, Kumaracoil, Tamil Nadu State, India, April 10-11, 2014.
Top 10 Cited Articles in VLSI Design & Communication Systems Research: Januar...VLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
Modeling of driver lane choice behavior with artificial neural networks (ann)...cseij
In parallel to the economic developments, the importance of road transportation was significantly
increased in Turkey. As a result of this, long-distance freight transportation gains more importance and
hence numbers of the heavy vehicles were significantly increased. Consequently, road surface deformations
are observed on the roads as the increasing freight transportation and climatic conditions influence the
road surface. Therefore, loss of functionality of the road surface is observed and drivers are much prone to
accident due to their driving characteristics as they can have more tendencies to change their lanes not to
pass through the deformation area. In this study, the lane changing behaviors of the drivers were
investigated and both Artificial Neural Network (ANN) and Linear Regression (LR) models were proposed
to simulate the driver behavior of lane changing who approach to a specific road deformation area. The
potential of ANN model for simulating the driver behavior was evaluated with successive comparison of the
model performances with LR model. While there was a slight performance increase for the ANN model with
respect to LR model, it is quite evident that, ANN models can play an important role for predicting the
driver behavior approaching a road surface deformation. It can be said that, approaching speed plays an
important factor on the lane changing behavior of a driver. This can be criticized by the fact that, drivers
with high approaching speeds more likely pass through the deformation to avoid the accidents while
changing their lanes with a high speed.
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 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.
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.
WATERSHED MODELING USING ARTIFICIAL NEURAL NETWORKS IAEME Publication
Artificial Neural Networks analysis was used for modeling rainfall-runoff relationship. A new Instantaneous ANN watershed model was built and tried herein using Walnut Gulch watershed (catchment) area. For modeling the instantaneous response of a catchment to a rainfall event an ANN model was built shown herein. The built model can represent the actual response using descritized
rainfall-runoff values, over a selected time interval (∆t). As this time interval decreases the actual response is more accurately modeled. This model was applied to one of the sub-catchment of Walnut Gulch watershed (sub-catchment No.9 (flume 11)). The model was found successful to represent the lag-time and time of runoff related to the hyetograph properties
Assessment of two Methods to study Precipitation PredictionAI Publications
Presipitation analysis plays an important role in hydrological studies. In this study, using 50 years of rainfall data and ARIMA model, critical areas of Iran were determined. For this purpose, annual rainfall data of 112 different synoptic stations in Iran were gathered. To summarize, it could be concluded that: ARIMA model was an appropriate tool to forecast annual rainfall. According to obtained results from relative error, five stations were in critical condition. At 45 stations accrued rainfalls with amounts of less than half of average in the 50-year period. Therefore, in these 45 areas, chance of drought is more than other areas of Iran.
APPLICATION OF GENE EXPRESSION PROGRAMMING IN FLOOD FREQUENCY ANALYSISMohd Danish
Flood frequency and its magnitude are essential for the proper design of hydraulics structures such as bridges, spillways, culverts, waterways, roads, railways, flood control structures and urban drainage systems. Since, flood is a very complex natural event depending upon characteristics of catchment, rainfall conditions and various other factors, thus its analytical modelling is very difficult to pursue. Recently, artificial intelligence techniques such as gene expression programming (GEP), artificial neural network (ANN) etc. have been found to be efficient in modelling complex problems in hydraulic engineering. The performance of GEP model has been reported to be better than that of the ANN. Moreover, GEP provides mathematical equation which makes it more superior over other soft computing techniques that do not give any analytical mathematical equation. Therefore, in present study, GEP is implemented in flood frequency analysis for typical Indian river gauging station. The results obtained in the present study are highly promising and suggest that GEP modelling is a versatile technique and represents an improved alternative to the more conventional approach for the flood frequency analysis.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional An...ijtsrd
The present study deals with the prediction of runoff of a river catchment of maharastra by using linear regressional analysis and self organizing maps by handling numerical data. The prediction is done by using past data record. A mathematical model has been developed for rainfall runoff correlation. Warish Khan | Adil Masood | Najib Hasan"Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional Analysis and Ann Tool Box" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7025.pdf http://www.ijtsrd.com/engineering/civil-engineering/7025/runoff-prediction-of-gharni-river-catchment-of-maharashtra-by-regressional-analysis-and-ann-tool-box/warish-khan
The flexibility and versatility of System Dynamics technique in optimization ...Samson Olakunle OJOAWO
Paper presented at the International Conference on Modeling, Optimization, and Computing, ICMOC 2014, N.I. University, Kumaracoil, Tamil Nadu State, India, April 10-11, 2014.
Top 10 Cited Articles in VLSI Design & Communication Systems Research: Januar...VLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
Modeling of driver lane choice behavior with artificial neural networks (ann)...cseij
In parallel to the economic developments, the importance of road transportation was significantly
increased in Turkey. As a result of this, long-distance freight transportation gains more importance and
hence numbers of the heavy vehicles were significantly increased. Consequently, road surface deformations
are observed on the roads as the increasing freight transportation and climatic conditions influence the
road surface. Therefore, loss of functionality of the road surface is observed and drivers are much prone to
accident due to their driving characteristics as they can have more tendencies to change their lanes not to
pass through the deformation area. In this study, the lane changing behaviors of the drivers were
investigated and both Artificial Neural Network (ANN) and Linear Regression (LR) models were proposed
to simulate the driver behavior of lane changing who approach to a specific road deformation area. The
potential of ANN model for simulating the driver behavior was evaluated with successive comparison of the
model performances with LR model. While there was a slight performance increase for the ANN model with
respect to LR model, it is quite evident that, ANN models can play an important role for predicting the
driver behavior approaching a road surface deformation. It can be said that, approaching speed plays an
important factor on the lane changing behavior of a driver. This can be criticized by the fact that, drivers
with high approaching speeds more likely pass through the deformation to avoid the accidents while
changing their lanes with a high speed.
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 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.
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.
WATERSHED MODELING USING ARTIFICIAL NEURAL NETWORKS IAEME Publication
Artificial Neural Networks analysis was used for modeling rainfall-runoff relationship. A new Instantaneous ANN watershed model was built and tried herein using Walnut Gulch watershed (catchment) area. For modeling the instantaneous response of a catchment to a rainfall event an ANN model was built shown herein. The built model can represent the actual response using descritized
rainfall-runoff values, over a selected time interval (∆t). As this time interval decreases the actual response is more accurately modeled. This model was applied to one of the sub-catchment of Walnut Gulch watershed (sub-catchment No.9 (flume 11)). The model was found successful to represent the lag-time and time of runoff related to the hyetograph properties
Assessment of two Methods to study Precipitation PredictionAI Publications
Presipitation analysis plays an important role in hydrological studies. In this study, using 50 years of rainfall data and ARIMA model, critical areas of Iran were determined. For this purpose, annual rainfall data of 112 different synoptic stations in Iran were gathered. To summarize, it could be concluded that: ARIMA model was an appropriate tool to forecast annual rainfall. According to obtained results from relative error, five stations were in critical condition. At 45 stations accrued rainfalls with amounts of less than half of average in the 50-year period. Therefore, in these 45 areas, chance of drought is more than other areas of Iran.
APPLICATION OF GENE EXPRESSION PROGRAMMING IN FLOOD FREQUENCY ANALYSISMohd Danish
Flood frequency and its magnitude are essential for the proper design of hydraulics structures such as bridges, spillways, culverts, waterways, roads, railways, flood control structures and urban drainage systems. Since, flood is a very complex natural event depending upon characteristics of catchment, rainfall conditions and various other factors, thus its analytical modelling is very difficult to pursue. Recently, artificial intelligence techniques such as gene expression programming (GEP), artificial neural network (ANN) etc. have been found to be efficient in modelling complex problems in hydraulic engineering. The performance of GEP model has been reported to be better than that of the ANN. Moreover, GEP provides mathematical equation which makes it more superior over other soft computing techniques that do not give any analytical mathematical equation. Therefore, in present study, GEP is implemented in flood frequency analysis for typical Indian river gauging station. The results obtained in the present study are highly promising and suggest that GEP modelling is a versatile technique and represents an improved alternative to the more conventional approach for the flood frequency analysis.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional An...ijtsrd
The present study deals with the prediction of runoff of a river catchment of maharastra by using linear regressional analysis and self organizing maps by handling numerical data. The prediction is done by using past data record. A mathematical model has been developed for rainfall runoff correlation. Warish Khan | Adil Masood | Najib Hasan"Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional Analysis and Ann Tool Box" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7025.pdf http://www.ijtsrd.com/engineering/civil-engineering/7025/runoff-prediction-of-gharni-river-catchment-of-maharashtra-by-regressional-analysis-and-ann-tool-box/warish-khan
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.
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. Keshav Kumar, Vivekanand Singh and Thendiyath. Roshni
http://www.iaeme.com/IJCIET/index.asp 38 editor@iaeme.com
Cite this Article: Keshav Kumar, Vivekanand Singh and Thendiyath. Roshni,
Efficacy of Neural Network in Rainfall-Runoff Modelling of Bagmati River Basin,
International Journal of Civil Engineering and Technology (IJCIET) 9(11), 2018, pp.
37–46.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11
1. INTRODUCTION
The rainfall-runoff process is highly nonlinear, time varying, spatially distributed, and not
easily described by simple models. A considerable amount of research effort in the area of
hydrology during the past few decades has been devoted towards development of computer
based models of rainfall-runoff processes. A rainfall-runoff model is used to simulate the
hydrologic response of a catchment to rainfall input. The estimation of runoff from a
catchment is required for the purposes such as design of storage facilities to assess the flood,
assessment of water available for municipal, agricultural or industrial purposes, planning
irrigation operations, estimating future dependable water supplies for power generation, wild
life protection etc. Many rainfall-runoff models have been developed over the years. These
models can be broadly divided in three categories: Black box models, Conceptual models and
physically based models.
The Black box models are based on transfer functions which relate inputs with outputs
and generally do not have any physical basis. The success of these models can be attributed
mainly to simple mathematics, minimum computational requirements and acceptable results.
Conceptual models require large computation for calibrating the parameters involved.
Application of distributed models requires large quantity of data compared to lumped models
and large computer resources for successful implementation. The time required to construct
these models is enormous and thus an alternative modeling technique is needed when detailed
modeling is not required. The linear time series models such as ARMA (Auto Regressive
Moving Average) have been developed to handle such situations because they are relatively
easy to implement.
In recent years, Artificial Neural Networks (ANNs) have become very popular for
prediction and forecasting in a number of areas including finance, power generation,
medicine, water resources and environmental science. The main reason is that ANNs can
represent any arbitrary nonlinear function given sufficient complexity of the trained neural
network (Dawson and Wilby, 1998). ANNs can find relationship between different input
samples and can group samples in similar way to cluster analysis. ANNs are able to
generalize a relationship from small sample of data, are robust in the presence of noisy or
missing inputs and can learn in response to changing environments. ANNs have been applied
widely in various aspects of hydrology such as rainfall-runoff modelling, stream flow
forecasting, ground water modeling, water quality, water management policy, precipitation
forecasting, hydrological time series, and reservoir operations (ASCE, 2000a). ASCE (2000a,
2000b) reported the applications of ANN in hydrology and water resources. ANN models
provided better results when compared with other conceptual SAC-SMA (Sacramento soil
moisture accounting) model (Hsu et al., 1995), autoregressive models (Raman and
Sunilkumar, 1995), ARMAX model (Fernando and Jayawardena, 1998), Volterra type
Functional Series Model (Sajikumar and Thandaveswara, 1999), multiple regression models
(Thirumalaiah and Dco, 2000) linear and non-linear regressive model (Elshorbagy et al.,
2000), and Conceptual models(Tokar and Markus, 2000) Sudhir et al. (2001), Kumar et al.
(2008), Kaltech (2008), Solaimani (2009), Nourani et al. (2011), Nourani et al. (2014);
Asadnia et al. (2014) have used the ANN model for the rainfall-runoff studies. Sudhir et al.
(2001), used ANN technique with back propagation algorithm for the development of rainfall
3. Efficacy of Neural Network in Rainfall-Runoff Modelling of Bagmati River Basin
http://www.iaeme.com/IJCIET/index.asp 39 editor@iaeme.com
runoff model. The statistical properties of data series such as auto, partial and cross
correlation values were used to select and appropriate input vector for the model
development. Kumar et al. (2008) examined the effectiveness of the rainfall runoff modeling
with ANNs by comparing their results with AREVIA model and concluded that ANN could
provide more accurate discharge forecasts than the traditional mentioned model. Kaltech
(2008) has introduced the interpretation diagram, Garson's algorithm, and randomization
approaches to understand the relationship learned by ANN model. The results indicated that
ANNs are promising tools not only in accurate modeling of complex processes but also in
providing insight from the learned relationship. Solaimani (2009) has demonstrated the
application of the feed forward back propagation for the rainfall forecasting with various
algorithms with performance of multi-layer perceptions.
Rajkumar et al. (2002), Tayfur and Singh (2006), S M Chen et al. (2013), Chen and Liu
(2013) have used the ANN model for the flood estimation. Rajkumar et al. (2002) applied
ANN for modelling daily flows during monsoon flood events for a catchment in India using
daily rainfall data as input vector of the network model. Tayfur and Singh (2006) used three-
layered feed forward neural network using sigmoid function with back propagation algorithm
to forecast the runoff and compared with fuzzy inference method. S M Chen et al. (2013) used
ANN technique with feed forward Natural network with back propagation algorithm for
runoff estimation and compared with Conventional Regression Analysis (CRA). They found
that Feed Forward Back Propagation network (FFBP) gave superior result than Conventional
Regression Analysis (CRA). Chen and Liu (2013) developed artificial neural network models
using back propagation algorithm and compared with multi-linear regression (MLR) model.
They found that ANN model gave better result than multi-linear regression MLR) model.
The results of any model application depend upon the quality of input data. In
undeveloped and developing countries, one frequently encounters a situation the input data
are of poor quality and inconsistent. Typically rain gauge network is inadequate which means
that the spatial variation of rainfall is poorly represented Enough secondary information may
not be available to improve the quality of input data or to remove inconsistency. Nevertheless,
modeling has to be carried out for a variety of purposes such as river basin planning,
hydrologic design of projects, flood forecasting, etc. ANN models are built using the input
and output observations without the detailed understanding of the complex physical laws
governing the process under investigation. It is also able to provide reasonably accurate model
for the process under investigation, as a large number of the applications in hydrology along
with the comparison of their predictive performance with other methods (Kaltech, 2008). The
results of various ANN models indicate that ANNs can be powerful tools in modeling the
rainfall-runoff process for various time scale, topography, and climatic patterns.
2. STUDY AREA AND DATA USED
The Bagmati River is a perennial river of Nepal and India, particularly of North Bihar It
originates from the Shivpuri range of hills in Nepal at latitude 270
47' N and longitude 850
17'E, and 16 km North-East of Kathmandu at an elevation of 1500 m above MSL. The
Bagmati flows southwesterly for about ten kilometres along the Kathmandu Valley which is
predominately rice-patties in terraces up the slopes. A number of resistant rock strata interrupt
the flow down the valley, among these, is the outcrop that the Pashupatinath Temple is built
upon. After passing the temple, the river flows south across the plain where it is joined by the
larger Monahara River and turns westward. In Kathmandu the river flows past several
important places. The river mixes with the Vishnumati (Bishnumati) after a number of curves
enters the Chobar Gorge.
4. Keshav Kumar, Vivekanand Singh and Thendiyath. Roshni
http://www.iaeme.com/IJCIET/index.asp 40 editor@iaeme.com
For this study, monthly rainfall data of 10 years i.e. from 2000 to 2009 at five rain gauge
stations namely Dheng, Kamtaul, Muzzafarpur, Benibag and Hayaghat in Bagmati river basin
have been used. Theisen polygons were drawn using these rain gauge stations to compute the
average depth of monthly rainfall. The discharge data measured at Hayaghat gauging site by
Central Water Commission (Central Water Commission) was used. These monthly rainfall
and runoff data were used to calibrate and validate the ANNs monthly model. Three sets of
data have been used for different combination of years. First set of data used is from 2000 to
2006 for the calibration and from 2007 to 2009 for validation. The second set of data is from
2004 to 2009 for calibration and from 2000 to 2003 for validation. The Third set of data is
from 2000 to 2009 for calibration and from 2007 to 2009 for validation.
3. METHODOLOGY
3.1. Artificial Neural Network Models
A typical ANN model consists of number of layers and nodes that are organized to a
particular structure. The commonly used neural network is three layered feed forward network
due to its general applicability to a variety of different problems (Hsu et al., 1995). The first
layer is input layer and its role is to pass the input variables onto the subsequent layers of the
network. The last layer consists of the output variables and is called as output layer. The
layers) in between the input and output layer are called as hidden layer(s) and the introduction
of this layer enhances the network's ability to model complex functions. The processing
elements in cach layer are called nodes. The information flow and processing in this network
is from input layer to hidden layer and from hidden layer to output layer. The number of
nodes in input and output layers is decided by the problem to be addressed. The number of
hidden layers and the number of nodes in each hidden layer are problem dependent and are
usually determined by a trial and error procedure. A synaptic weight is assigned to each link
to represent the relative connection strength of two nodes at both ends in predicting the input-
output relationship. The output yi of any node j, is given by
j
m
i
iij bWXfy
1
. (3.1)
where Xi is the input received at node j, W, is the input connection pat way weights, m is
the total numbers of inputs to node j, bj is the node threshold and function f is called an
activation function. It determines the response of a node to the total input signal it receives
and is given as the sigmoid function (Dawson and Wilby, 1998).
xe
xf
1
1
)( (3.2)
The characteristics of a sigmoid function are that it is bounded above and below, it is
monotonically increasing, and it is continuous and differentiable everywhere. Any nonlinear
process can be mapped using this sigmoidal function (ASCE, 2000a). Back propagation, the
most popular algorithm used for the training of the Feed Forward ANNs by Hsu et al., (1995)
and Burian et al., 2001), is used for training the ANN. In this process, each input pattern of
the training data set is passed through the network from the input layer to output layer. The
network output is compared with the desired target output, and an error is computed as
P P
i
t
i
yE 2
(3.3)
Where, P is the number of training patterns; ti is a component of the desired output T; p is
the number of output nodes; and yi is the relevant output of ANN.
5. Efficacy of Neural Network in Rainfall-Runoff Modelling of Bagmati River Basin
http://www.iaeme.com/IJCIET/index.asp 41 editor@iaeme.com
This error is propagated backward through the network to each node, and correspondingly
the connection weights are adjusted based on the equation (ASCE, 2000a)
1
nw
w
E
nw ij
ij
ij (3.4)
Where, )(nwij and )1( nwij are weight increments between node i and j during nth and
(n-1)th epoch or pass.
Due to the boundation of the sigmoid function between 0 and 1, all input values should be
converted to the range between 0 and 1 before passing into a neural network (Smith and Eli,
1995). The output from the ANN should be denormalized to provide meaningful results.
Equation used to normalize the data set is
)()( b
i
Mina
i
Max
R
i
N i
(3.5)
Where, Ni is the subsequent standardized value calculated for node i; Ri is the real value
applied to node i; Maxi is the maximum value of all values applied to node i; Mini is the
minimum value of all values applied to node i. The a and b are constants to define the range
of normalization.
3.2. Model Performance Evaluation Criteria
The performance of a model can be evaluated in terms of several characteristics. Root
mean square error (RMSE) and coefficient of correlation (R) are the numerical
performance indicators used to compare the models. They are defined as follows:
K
K
k
yt
RMSE
1
2
(3.6)
22
)(
YT
TY
RncorrelatiooftCoefficien (3.7)
Where, t is the observed data; y is computed data; K is the number of observations;
yyY in which y is the mean of the computed data and ttT in which t is the mean
of the observed data.
To get the optimized structure for the neural network model, several combinations of
inputs were trained, but the best one is: rainfall (t-2), rainfall (t-I) and rainfall (t). In this case
the output neuron was runoff (t). It was noticed that the best convergence was achieved for the
above combination with the error tolerance, the learning parameter, neurons in hidden layer
and number of cycles as 0.1, 0.9, 9 and 9000 respectively. The coefficient of correlation was
0.910 and root mean square error (RMSE) was 119. Then the weights for this best trained
structure were frozen to evaluate the trained network. The weights for the best trained
network structure were collected from the training module of the Back Propagation simulator.
The monthly rainfall and runoff data were normalized and the data set of input vector was
prepared according to the best trained neural network structure. The runoff was computed
using this network and the weight vector for this trained network structure. The computed
runoff values were denormalized and compared with the observed runoff values.
6. Keshav Kumar, Vivekanand Singh and Thendiyath. Roshni
http://www.iaeme.com/IJCIET/index.asp 42 editor@iaeme.com
4. RESULTS AND DISCUSSIONS
First of all, the first set of data used for the calibration is from the years 2000 to 2006 and for
the validation from the years 2007 to 2009 for the development of model. The calibration and
validation of first data set is shown in figure 4.1 and figure 4.2 respectively. From the
calibrated result of first set of data, it was found that all the peaks of the computed runoff
hydrograph were not matching well with the observed peaks. The coefficient of correlation
was 0.910 and RMSE was 119. The coefficient of correlation was good, but the root mean
square error was large.
Figure 4.1 Calibrated Results of the best combination of ANN by BP algorithm for 2000 to 2006.
Figure 4.2 Validation result of discharge by ANN model for 2007 to 2009
The model was validated with this set of data and it was found that all the peaks of the
computed runoff data are not matching with the observed peaks, but first computed peak is
nearly same whereas the second peak is high and there is slight lag in third observed peak.
This lag shows some error in the observed discharge data. The coefficient of correlation and
RMSE were 0.949 and 83 respectively. Coefficient of correlation was satisfactory and root
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7. Efficacy of Neural Network in Rainfall-Runoff Modelling of Bagmati River Basin
http://www.iaeme.com/IJCIET/index.asp 43 editor@iaeme.com
mean square error was low, so the matching was not satisfactory in the validation period, and
the peaks of computed hydrograph were not matched well, so another set of data were used.
Model was then calibrated with the second set of data from the years 2004 to 2009 and for
the validation from the years 2000 to 2003. The calibration and validation of second data set
is shown in figure 4.3 and figure 4.4 respectively. It was found that again all the computed
peaks are not matching well but results are more closure to the observed peaks of runoff
hydrograph. The coefficient of correlation between the observed and computed runoff was
0.957 and root mean square error was correlation is high and the root mean square error is
also low.
Figure 4.3 Calibrated Results of the best combination of ANN by BP algorithm for 2004 to 2009.
In the case of validation, it was found that all the peaks of the computed runoff data were
not matching satisfactorily with the observed peaks. The coefficient of correlation was 0.896
whereas the RMSE was 119. Coefficient of correlation is satisfactory but root mean square
error is high. From the above two sets of data it was concluded that there was some problem
in the beginning stage of the data and that is why the RMSE were high, whereas the statistical
parameters show very good results in the last stage of the data. This may be due to the small
span of the data used due to non-availability.
Figure 4.4 Validation result of discharge by ANN model for 2000 to 2003
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0 6 12 18 24 30 36 42 48 54 60 66 72 78
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8. Keshav Kumar, Vivekanand Singh and Thendiyath. Roshni
http://www.iaeme.com/IJCIET/index.asp 44 editor@iaeme.com
Then, third set of data from the years 2000 to 2009 and from the years 2007 to 2009 were
used for calibration and validation. The calibration and validation of third data set is shown in
figure 4.5 and figure 4.6 respectively. It was found that this set of data gave good result in
calibration as well as in validation as compared to the first two set of data. Figure 4.5 presents
the computed and observed hydrographs for calibration and Figure 4.6 presents the computed
hydrograph for validation. From Fig. 4.6, it can be seen that the calibrated peaks are more or
less same and coefficient of correlation was 0.915 and RMSE was 97. The coefficient of
correlation is satisfactory and the root mean square error is small. For validation, the
coefficient of correlation was 0.947 whereas RMSE was 81. Coefficient of correlation is again
satisfactory but root mean square error is little bit high. It was also observed that the matching
of the peaks was good in the validation period as compared to the earlier two set of data. The
finally the third set of data were used for the model development.
Figure 4.5 Calibrated Results of the best combination of ANN by BP algorithm for 2000 to 2009.
Figure Error! No text of specified style in document..6 Validation result of discharge by ANN model
for 2007 to 2009
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9. Efficacy of Neural Network in Rainfall-Runoff Modelling of Bagmati River Basin
http://www.iaeme.com/IJCIET/index.asp 45 editor@iaeme.com
5. CONCLUSIONS
In this study, an ANN model for the rainfall-runoff process was developed for Bagmati basin.
Three layered feed forward network structure was used to model the process. Back
propagation algorithm was used to train the ANN model. Fifteen 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. It
was observed from the training results that the combination of rainfall(t-2), rainfallt-1) and
rainfall(t) as input and runoffit) as output was the best combination compared to other
combinations with high coefficient of correlation and low root mean square error. In the
training of objective is to achieve a global minimum error on the whole length of the data.
Training the model with long record of data, which contain more extreme events, can reduce
the large variations in the statistical parameter. It was observed from the training and
validation results that ANNs are good in learning the underlying process in rainfall runoff
relationship. 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. However, the entire
study was based oi a short period of data. If a proper, longer data set is available, it will be
better to model rainfall-runoff processes for longer periods in seasonal and annual scales. The
phenomena can be modelled and studied for watersheds where rainfall and stage data are
measured directly and independently.
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