Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region

Regarding different aspects of management of drainage basins and droughts, prediction of evaporation is very important. Evaporation is an essential part of the water cycle and plays an important role in the evaluation of climatic characteristics of any region. The purpose of this research is to pred...

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Autores principales:	Sina Shahi, Sayed Farhad Mousavi, Khosrow Hosseini
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	pan evaporation evaporation prediction artificial neural network damghan Technology T
Acceso en línea:	https://doaj.org/article/29fdc41f0c154d2ba1c177774be0b5c9
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spelling	oai:doaj.org-article:29fdc41f0c154d2ba1c177774be0b5c92021-11-11T11:41:52ZSimulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region2588-287210.22115/scce.2021.286933.1321https://doaj.org/article/29fdc41f0c154d2ba1c177774be0b5c92021-07-01T00:00:00Zhttp://www.jsoftcivil.com/article_136767_e22203b5c6ba0df00dac7a27e3d05a5b.pdfhttps://doaj.org/toc/2588-2872Regarding different aspects of management of drainage basins and droughts, prediction of evaporation is very important. Evaporation is an essential part of the water cycle and plays an important role in the evaluation of climatic characteristics of any region. The purpose of this research is to predict daily pan evaporation rate of Damghan city using an artificial neural network model. The data applied in this research are daily minimum and maximum temperatures (Tmin and Tmax), average relative humidity (RHmean), wind speed (WS), sunny hours (n), air pressure (PA) and evaporation during the statistical time period of 16 years (2002-2018). Also, the multilayer perceptron was used as a non-linear method to simulate evaporation. Since the units of the inputs and outputs of the prediction model were different, all the data were normalized. In the multilayer perceptron model, 7 different scenarios were considered. About 70 and 30 percentage of the data were used for training and testing, respectively. The model was analyzed using appropriate statistics such as mean square error (RMSE), coefficient of determination (R2),mean absolute error (MAE) and mean square error (MSE). Results showed that the seventh scenario including Tmin, Tmax, RHmean, WS, ‎ n ‎, and PA proved to be the superior scenario among others. The values of RMSE, R2, MAE and MSE for the superior scenario were 2.75 mm/day, 0.8030, 1.88 mm/day and (mm/day)2, respectively.Sina ShahiSayed Farhad MousaviKhosrow HosseiniPouyan Pressarticlepan evaporationevaporation predictionartificial neural networkdamghanTechnologyTENJournal of Soft Computing in Civil Engineering, Vol 5, Iss 3, Pp 75-87 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pan evaporation evaporation prediction artificial neural network damghan Technology T
spellingShingle	pan evaporation evaporation prediction artificial neural network damghan Technology T Sina Shahi Sayed Farhad Mousavi Khosrow Hosseini Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region
description	Regarding different aspects of management of drainage basins and droughts, prediction of evaporation is very important. Evaporation is an essential part of the water cycle and plays an important role in the evaluation of climatic characteristics of any region. The purpose of this research is to predict daily pan evaporation rate of Damghan city using an artificial neural network model. The data applied in this research are daily minimum and maximum temperatures (Tmin and Tmax), average relative humidity (RHmean), wind speed (WS), sunny hours (n), air pressure (PA) and evaporation during the statistical time period of 16 years (2002-2018). Also, the multilayer perceptron was used as a non-linear method to simulate evaporation. Since the units of the inputs and outputs of the prediction model were different, all the data were normalized. In the multilayer perceptron model, 7 different scenarios were considered. About 70 and 30 percentage of the data were used for training and testing, respectively. The model was analyzed using appropriate statistics such as mean square error (RMSE), coefficient of determination (R2),mean absolute error (MAE) and mean square error (MSE). Results showed that the seventh scenario including Tmin, Tmax, RHmean, WS, ‎ n ‎, and PA proved to be the superior scenario among others. The values of RMSE, R2, MAE and MSE for the superior scenario were 2.75 mm/day, 0.8030, 1.88 mm/day and (mm/day)2, respectively.
format	article
author	Sina Shahi Sayed Farhad Mousavi Khosrow Hosseini
author_facet	Sina Shahi Sayed Farhad Mousavi Khosrow Hosseini
author_sort	Sina Shahi
title	Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region
title_short	Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region
title_full	Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region
title_fullStr	Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region
title_full_unstemmed	Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region
title_sort	simulation of pan evaporation rate by ann artificial intelligence model in damghan region
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/29fdc41f0c154d2ba1c177774be0b5c9
work_keys_str_mv	AT sinashahi simulationofpanevaporationratebyannartificialintelligencemodelindamghanregion AT sayedfarhadmousavi simulationofpanevaporationratebyannartificialintelligencemodelindamghanregion AT khosrowhosseini simulationofpanevaporationratebyannartificialintelligencemodelindamghanregion
_version_	1718439111487913984

Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region

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