Modeling of daily pan evaporation using partial least squares regression  

作　　者：ABUDU Shalamu CUI ChunLiang J. Phillip KING Jimmy MORENO A. Salim BAWAZIR 

机构地区：[1]Xinjiang Water Resources Research Institute, Urumqi 830049, China [2]Civil Engineering Department, New Mexico State University, Las Cruces, New Mexico, 88001, USA

出　　处：《Science China(Technological Sciences)》2011年第1期163-174,共12页中国科学（技术科学英文版）

基　　金：supported in part by the National Natural Science Founda-tion of China (Grant Nos.51069017,41071026);their sincere appreciation of the reviewers’ valuable suggestions and comments in improving the quality of this paper

摘　　要：This study presented the application of partial least squares regression （PLSR） in estimating daily pan evaporation by utilizing the unique feature of PLSR in eliminating collinearity issues in predictor variables. The climate variables and daily pan evaporation data measured at two weather stations located near Elephant Butte Reservoir, New Mexico, USA and a weather station located in Shanshan County, Xinjiang, China were used in the study. The nonlinear relationship between climate variables and daily pan evaporation was successfully modeled using PLSR approach by solving collinearity that exists in the climate variables. The modeling results were compared to artificial neural networks （ANN） models with the same input variables. The resuits showed that the nonlinear equations developed using PLSR has similar performance with complex ANN approach for the study sites. The modeling process was straightforward and the equations were simpler and more explicit than the ANN black-box models.

关 键 词：MODELING daily pan evaporation partial least squares regression artificial neural networks meteorological data 

分 类 号：P333.1[天文地球—水文科学]