Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns

Sungwon Kim (), Jalal Shiri, Ozgur Kisi and Vijay Singh

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2013, vol. 27, issue 7, 2267-2286

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. Copyright Springer Science+Business Media Dordrecht 2013

Keywords: Multilayer perceptron; Generalized regression; Adaptive neuro-fuzzy inference system; Pan evaporation; Lag-time patterns (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11269-013-0287-2

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