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王伦澈 地学院 J HYDROL,January 2017. Pan evaporation modeling using six different heuristic computing methods in different climates of China

Pan evaporation modeling using six different heuristic computing methods in different climates of China

Wang, Lunche*;Kisi, Ozgur ;Zounemat-Kermani, Mohammad;Li, Hui

JOURNAL OF HYDROLOGY

Volume 544, January 2017, Pages 407–427

DOI:10.1016/j.jhydrol.2016.11.059

http://dx.doi.org/10.1016/j.jhydrol.2016.11.059

Abstract

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modelingEpusing limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthlyEp. Long-term climatic data at various sites crossing a wide range of climates during 1961–2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthlyEpat most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded thatEpcould be successfully predicted using above models in hydrological modeling studies. 全文链接