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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2016-247
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
23 May 2016
Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.
Comparison of six different soft computing methods in modeling evaporation in different climates
Lunche Wang1, Ozgur Kisi2, Mohammad Zounemat-Kermani3, and Yiqun Gan4 1Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
2Canik Basari University, Faculty of Architecture and Engineering, Civil Engineering Department, Samsun, Turkey
3Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
4School of Enviromental studies, China University of Geosciences, Wuhan 430074, China
Abstract. Evaporation plays important roles in regional water resources management,terrestrial ecological process and regional climate change. This study investigated the abilities of six different soft computing methods, Multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at eight stations in different climates, air temperature (Ta), solar radiation (Rg), sunshine hours (Hs), relative humidity (RH) and wind speed (Ws) during 1961–2000 are used for model development and validation. The first part of applications focused on testing and comparing the model accuracies using different local input combinations. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations, while GRNN model performed better in Tibetan Plateau. The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. Generalized models were also developed and tested with data of eight stations. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ, CQ and HK stations.

Citation: Wang, L., Kisi, O., Zounemat-Kermani, M., and Gan, Y.: Comparison of six different soft computing methods in modeling evaporation in different climates, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-247, 2016.
Lunche Wang et al.
Lunche Wang et al.

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Short summary
This study investigated and compared the abilities of six different soft computing techniques, MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS, and two regression methods, MLR and SS, in modeling Ep using different climatic input combinations at different climatic zones. It is revealed that the MLP models are the most appropriate for predicting Ep using limited climatic inputs in different climates, which can be practically adopted in the field of water resources management.
This study investigated and compared the abilities of six different soft computing techniques,...
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