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Open AccessArticle

Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques

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Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51431, Saudi Arabia
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Graduate Student, Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51431, Saudi Arabia
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Department of Civil Engineering, University of Engineering and Technology, Taxila 47080, Pakistan
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Civil Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
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Department of Civil Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia
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Author to whom correspondence should be addressed.
Academic Editor: Renato Morbidelli
Water 2021, 13(6), 793; https://doi.org/10.3390/w13060793
Received: 8 February 2021 / Revised: 10 March 2021 / Accepted: 11 March 2021 / Published: 14 March 2021
(This article belongs to the Section Hydrology and Hydrogeology)
The evaporation losses are very high in warm-arid regions and their accurate evaluation is vital for the sustainable management of water resources. The assessment of such losses involves extremely difficult and original tasks because of the scarcity of data in countries with an arid climate. The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). The results from five types of ANN models with different training functions were compared to find the best possible training function. The impact of using various input variables was investigated as an original contribution of this research. The average temperature and mean wind speed were found to be the most influential parameters. The estimation of parameters for Penman and Hamon’s equations was quite a daunting task. These parameters were estimated using a state of the art optimization algorithm, namely General Reduced Gradient Technique. The results of the Penman and Hamon’s equations, ANN, and ANFIS were compared. Thirty-eight years (from 1980 to 2018) of manually recorded pan-evaporation data regarding mean daily values of a month, including the relative humidity, wind speed, sunshine duration, and temperature, were collected from three gauging stations situated in Al Qassim, Saudi Arabia. The Nash and Sutcliffe Efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of pan-evaporation modeling techniques. The study shows that the ANFIS simulation results were better than those of ANN and Penman and Hamon’s equations. The findings of the present research will help managers, engineers, and decision makers to sustainability manage natural water resources in warm-arid regions. View Full-Text
Keywords: reduced gradient; warm-arid; pan-evaporation; Neural Networks; Neuro Fuzzy; relative humidity reduced gradient; warm-arid; pan-evaporation; Neural Networks; Neuro Fuzzy; relative humidity
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MDPI and ACS Style

Ghumman, A.R.; Jamaan, M.; Ahmad, A.; Shafiquzzaman, M.; Haider, H.; Al Salamah, I.S.; Ghazaw, Y.M. Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques. Water 2021, 13, 793. https://doi.org/10.3390/w13060793

AMA Style

Ghumman AR, Jamaan M, Ahmad A, Shafiquzzaman M, Haider H, Al Salamah IS, Ghazaw YM. Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques. Water. 2021; 13(6):793. https://doi.org/10.3390/w13060793

Chicago/Turabian Style

Ghumman, Abdul R.; Jamaan, Mohammed; Ahmad, Afaq; Shafiquzzaman, Md.; Haider, Husnain; Al Salamah, Ibrahim S.; Ghazaw, Yousry M. 2021. "Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques" Water 13, no. 6: 793. https://doi.org/10.3390/w13060793

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