Development of a support‐vector‐machine‐based model for daily pan evaporation estimation

Evaporation estimation is an important issue in water resources management. In this article, a four-season model with optimal input combination is proposed to estimate the daily evaporation. First, the model based on support vector machine (SVM) coupled with an input determination process is used to determine the optimal combination of input variables. Second, a comparison of the SVM-based model with the model based on back-propagation network (BPN) is made to demonstrate the superiority of the SVM-based model. In addition, season data are used to construct the SVM-based four-season model to further improve the daily evaporation estimation. An application is conducted to demonstrate the performance of the proposed model. Results show that the SVM-based model can select the optimal input combination with physical mechanism. The SVM-based model is more appropriate than the BPN-based model because of its higher accuracy, robustness and efficiency. Moreover, the improvement due to the use of the four-season model increases from 3.22% to 15.30% for RMSE and from 4.84% to 91.16% for CE, respectively. In conclusion, the SVM-based model coupled with the proposed input determination process should be used to select input variables. The proposed four-season SVM-based model with optimal input combination is recommended as an alternative to the existing models. The proposed modelling technique is expected to be useful to improve the daily evaporation estimation. Copyright © 2012 John Wiley & Sons, Ltd.

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