Chang, FJFJChangWEI-ZEN SUNChung, CHCHChung2023-02-102023-02-1020130262-6667https://scholars.lib.ntu.edu.tw/handle/123456789/627881Evaporation is an important reference for managers of water resources. This study proposes a hybrid model (BD) that combines back-propagation neural networks (BPNN) and dynamic factor analysis (DFA) to simultaneously precisely estimate pan evaporation at multiple meteorological stations in northern Taiwan through incorporating a large number of meteorological data sets into the estimation process. The DFA is first used to extract key meteorological factors that are highly related to pan evaporation and to establish the common trend of pan evaporation among meteorological stations. The BPNN is then trained to estimate pan evaporation with the inputs of the key meteorological factors and evaporation estimates given by the DFA. The BD model successfully inherits the advantages from the DFA and BPNN, and effectively enhances its generalization ability and estimation accuracy. The results demonstrate that the proposed BD model has good reliability and applicability in simultaneously estimating pan evaporation for multiple meteorological stations. © 2013 Copyright 2013 IAHS Press.pan evaporation; artificial neural network; dynamic factor analysis; back-propagation neural network; meteorological stations; REFERENCE EVAPOTRANSPIRATION; COMMON TRENDS; CLIMATIC DATA; COMPLEMENTARY RELATIONSHIP; SPATIAL-DISTRIBUTION; SENSITIVITY-ANALYSIS; MODEL; EQUATIONS; OPTIMIZATION; ALGORITHMjournal article10.1080/02626667.2013.7754472-s2.0-84877578251WOS:000318289800006https://scholars.lib.ntu.edu.tw/handle/123456789/448915