Artificial neural network models for reference evapotranspiration in an arid area of northwest China |
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| Institution: | 1. Centre for Agricultural Water Research in China, China Agricultural University, Beijing 100083, PR China;2. College of Water Science and Engineering, Yangzhou University, Yangzhou 225009, PR China;3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, PR China;1. Dept. of Civil Engineering, Indian Institute of Technology, Guwahati 781039, India;2. Dept. of Water Resources Development Management, Indian Institute of Technology, Roorkee 247667, India;3. Dept. of Bioresource Engineering, McGill University, 21 111 Lakeshore Road, Ste. Anne de Bellevue, QC H9X 3V9, Canada;1. University of Nis, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Nis, Serbia;2. University of Nis, Faculty of Civil Engineering and Architecture, Aleksandra Medvedeva 14, 18000 Nis, Serbia;3. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Kuala Lumpur 50603, Malaysia;4. Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;5. Institute of Ocean and Earth Sciences (IOES), University of Malaya, Kuala Lumpur 50603, Malaysia;6. Department of Civil Engineering, Razi University, Kermanshah, Iran;1. Laboratory for Research in Applied Biochemistry, Section of Postgraduate Studies and Research, Department of Basic Disciplinary Training, Higher School of Medicine, National Polytechnic Institute, Mexico City, Mexico;2. Research Laboratory in Chronic Degenerative Diseases, Section of Postgraduate Studies and Research, Higher School of Medicine, National Polytechnic Institute, Mexico City, Mexico;3. Department of Basic Interdisciplinary Training, Interdisciplinary Center for Health Sciences-Santo Tomás Unit, National Polytechnic Institute, Mexico City, Mexico;1. Alamoudi Water Chair, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia;2. Agricultural Engineering Department, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia;3. Agricultural Engineering Research Institute (AEnRI), Agricultural Research Center, P.O. Box 256, Giza, Egypt |
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| Abstract: | We trained and tested artificial neural network (ANN) models for reference evapotranspiration (ET0) using 50 years’ meteorological data from three stations in northwest China. Multiple linear regressions (MLRs), the Penman equation, and two empirical equations were used to compare the performance of the ANNs. A connection weight method was used to quantify the importance of climate factors in performance. In addition, the error changes of the ANNs with seasons were evaluated according to absolute error, variance, and coefficient of variance. Results showed that in arid and semi-arid areas, the ANNs in which the climate data were used successfully estimated ET0, and the ANNs with five inputs were more accurate than those with four or three. Relative to the MLRs, the Penman equation, and empirical equations, the ANNs exhibited high precision. Maximum air temperature, minimum air temperature, and relative humidity were the most crucial input of ANN-based ET0 estimation for arid and semi-arid areas. In the study area, the importance of these three climate factors accounted respectively for 39.82–46.64%, 28.48–33.46%, and 10.73–26.17% to estimation of ET0. Generally, ANNs underestimated ET0 from January to July and overestimated it from August to December. |
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