期刊
WATER RESOURCES MANAGEMENT
卷 27, 期 7, 页码 2267-2286出版社
SPRINGER
DOI: 10.1007/s11269-013-0287-2
关键词
Multilayer perceptron; Generalized regression; Adaptive neuro-fuzzy inference system; Pan evaporation; Lag-time patterns
资金
- Dongyang University
This study develops three neural networks models for estimating daily pan evaporation (PE) in South Korea: multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and adaptive neuro-fuzzy inference system (ANFIS). Daily PE was estimated at Daegu and Ulsan stations using temperature-based, radiation-based, sunshine duration-based and merged input combinations under lag-time patterns. Daily evaporation values computed by the models using merged inputs agreed with observed values. Comparison was also made between the neural networks models and multiple linear regression model (MLRM), which showed the superiority of MLP-NNM, GRNNM, and ANFIS over MLRM. It is concluded that the applied neural networks models can be successfully employed for estimating daily PE in South Korea.
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