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Article

Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China

1
School of Environmental Science and Tourism, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, China
2
School of Earth Sciences and Engineering, Hohai University, Xikang Road No.1, Nanjing 210098, China
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Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China
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School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
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School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Land 2020, 9(7), 229; https://doi.org/10.3390/land9070229
Received: 14 June 2020 / Revised: 14 July 2020 / Accepted: 14 July 2020 / Published: 16 July 2020
Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R2 = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (Ro) and air temperature (T)) SS empirical model (R2 = 0.80). Similarly. the two-input ANN models (maximum R2 = 0.83) also generally performed better than the two-input (Ro and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province. View Full-Text
Keywords: pan evapotranspiration; climatic characteristics; artificial neural network; Henan province pan evapotranspiration; climatic characteristics; artificial neural network; Henan province
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MDPI and ACS Style

Zhang, M.; Su, B.; Nazeer, M.; Bilal, M.; Qi, P.; Han, G. Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China. Land 2020, 9, 229. https://doi.org/10.3390/land9070229

AMA Style

Zhang M, Su B, Nazeer M, Bilal M, Qi P, Han G. Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China. Land. 2020; 9(7):229. https://doi.org/10.3390/land9070229

Chicago/Turabian Style

Zhang, Miao; Su, Bo; Nazeer, Majid; Bilal, Muhammad; Qi, Pengcheng; Han, Ge. 2020. "Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China" Land 9, no. 7: 229. https://doi.org/10.3390/land9070229

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