Evaluation of soft computing and regression-based techniques for the estimation of evaporation

The estimation of evaporation in the field as well as the regional level is required for the efficient planning and management of water resources. In the present study, artificial neural network (ANN) and multiple linear regression (MLR)-based models were developed to estimate the pan evaporation on...

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Autores principales:	Aparajita Singh, R. M. Singh, A. R. Senthil Kumar, Ashish Kumar, Subodh Hanwat, V. K. Tripathi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial neural network feedforward neural network log-sigmoid activation function multilinear regression Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/5755516ed93e41b191e188e453a95ed5
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spelling	oai:doaj.org-article:5755516ed93e41b191e188e453a95ed52021-11-05T18:40:40ZEvaluation of soft computing and regression-based techniques for the estimation of evaporation2040-22442408-935410.2166/wcc.2019.101https://doaj.org/article/5755516ed93e41b191e188e453a95ed52021-02-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/1/32https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354The estimation of evaporation in the field as well as the regional level is required for the efficient planning and management of water resources. In the present study, artificial neural network (ANN) and multiple linear regression (MLR)-based models were developed to estimate the pan evaporation on the basis of one day-lagged rainfall (Pt−1), one day-lagged relative humidity (RHt−1), current day maximum temperature (Tmax) and minimum temperature (Tmin). These were selected as the most effective parameters on the basis of cross-correlation. The performance of models was evaluated using correlation coefficient (r), root-mean-square error (RMSE) and Nash–Sutcliffe efficiency (coefficient of efficiency, CE) during calibration and validation periods. Based on the comparison, the ANN model (4-9-1), with sigmoid as activation function and Levenberg–Marquardt as a learning algorithm, was selected as the best performing model among all ANN models. The values of r, CE and RMSE for training and validation periods were found as 0.885, 0.785 and 1.00 mm/day and 0.889, 0.782 and 1.01 mm/day, respectively, through the ANN model (4-9-1). The values of r, CE and RMSE for training and validation periods were found as 0.835, 0.698 and 1.19 mm/day and 0.866, 0.750 and 1.15 mm/day, respectively, through the selected MLR model. Based on the sensitivity analysis, RHt−1 is selected as the most effective parameter followed by Pt−1, Tmax and Tmin. The developed model can be utilized as an alternative for the estimation of the evaporation at the regional level with limited input data.Aparajita SinghR. M. SinghA. R. Senthil KumarAshish KumarSubodh HanwatV. K. TripathiIWA Publishingarticleartificial neural networkfeedforward neural networklog-sigmoid activation functionmultilinear regressionEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 1, Pp 32-43 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network feedforward neural network log-sigmoid activation function multilinear regression Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	artificial neural network feedforward neural network log-sigmoid activation function multilinear regression Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Aparajita Singh R. M. Singh A. R. Senthil Kumar Ashish Kumar Subodh Hanwat V. K. Tripathi Evaluation of soft computing and regression-based techniques for the estimation of evaporation
description	The estimation of evaporation in the field as well as the regional level is required for the efficient planning and management of water resources. In the present study, artificial neural network (ANN) and multiple linear regression (MLR)-based models were developed to estimate the pan evaporation on the basis of one day-lagged rainfall (Pt−1), one day-lagged relative humidity (RHt−1), current day maximum temperature (Tmax) and minimum temperature (Tmin). These were selected as the most effective parameters on the basis of cross-correlation. The performance of models was evaluated using correlation coefficient (r), root-mean-square error (RMSE) and Nash–Sutcliffe efficiency (coefficient of efficiency, CE) during calibration and validation periods. Based on the comparison, the ANN model (4-9-1), with sigmoid as activation function and Levenberg–Marquardt as a learning algorithm, was selected as the best performing model among all ANN models. The values of r, CE and RMSE for training and validation periods were found as 0.885, 0.785 and 1.00 mm/day and 0.889, 0.782 and 1.01 mm/day, respectively, through the ANN model (4-9-1). The values of r, CE and RMSE for training and validation periods were found as 0.835, 0.698 and 1.19 mm/day and 0.866, 0.750 and 1.15 mm/day, respectively, through the selected MLR model. Based on the sensitivity analysis, RHt−1 is selected as the most effective parameter followed by Pt−1, Tmax and Tmin. The developed model can be utilized as an alternative for the estimation of the evaporation at the regional level with limited input data.
format	article
author	Aparajita Singh R. M. Singh A. R. Senthil Kumar Ashish Kumar Subodh Hanwat V. K. Tripathi
author_facet	Aparajita Singh R. M. Singh A. R. Senthil Kumar Ashish Kumar Subodh Hanwat V. K. Tripathi
author_sort	Aparajita Singh
title	Evaluation of soft computing and regression-based techniques for the estimation of evaporation
title_short	Evaluation of soft computing and regression-based techniques for the estimation of evaporation
title_full	Evaluation of soft computing and regression-based techniques for the estimation of evaporation
title_fullStr	Evaluation of soft computing and regression-based techniques for the estimation of evaporation
title_full_unstemmed	Evaluation of soft computing and regression-based techniques for the estimation of evaporation
title_sort	evaluation of soft computing and regression-based techniques for the estimation of evaporation
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/5755516ed93e41b191e188e453a95ed5
work_keys_str_mv	AT aparajitasingh evaluationofsoftcomputingandregressionbasedtechniquesfortheestimationofevaporation AT rmsingh evaluationofsoftcomputingandregressionbasedtechniquesfortheestimationofevaporation AT arsenthilkumar evaluationofsoftcomputingandregressionbasedtechniquesfortheestimationofevaporation AT ashishkumar evaluationofsoftcomputingandregressionbasedtechniquesfortheestimationofevaporation AT subodhhanwat evaluationofsoftcomputingandregressionbasedtechniquesfortheestimationofevaporation AT vktripathi evaluationofsoftcomputingandregressionbasedtechniquesfortheestimationofevaporation
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Evaluation of soft computing and regression-based techniques for the estimation of evaporation

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