Abstract
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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Acknowledgements
The first author, Sungwon Kim, would like to acknowledge the financial support from Dongyang University for the sabbatical levee at Texas A & M University during the course of this study.
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Kim, S., Shiri, J., Kisi, O. et al. Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns. Water Resour Manage 27, 2267–2286 (2013). https://doi.org/10.1007/s11269-013-0287-2
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DOI: https://doi.org/10.1007/s11269-013-0287-2