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SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation

  • Behrooz KeshtegarEmail author
  • Salim HeddamEmail author
  • Abderrazek Sebbar
  • Shun-Peng Zhu
  • Nguyen-Thoi Trung
Research Article
  • 4 Downloads

Abstract

In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (Epan). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process. Results obtained using the proposed SVR_RSM was compared with those obtained using the RSM, SVR, and the well-known multilayer perceptron neural network (MLPNN) models. Climatic variables including maximum and minimum temperatures (Tmax, Tmin), wind speed (U2), and relative humidity (H%), and the periodicity represented by the month number (α) were selected for predicting the monthly Epan measured with the standard class A evaporation pan. Data was collected at six climatic stations located at the northern East of Algeria. The performances of the proposed models were compared using the RMSE, MAE, modified index of agreement (d), coefficient of correlation (R), and modified Nash and Sutcliffe efficiency (NSE). Using various input combination, the results show that the hybrid SVR_RSM model performed better than all the proposed models. Overall, better accuracy was observed when the model contained the periodicity (α), and it was demonstrated that the best accuracy was obtained using only Tmax and Tmin, coupled with the periodicity.

Keywords

Monthly pan evaporation Hybrid intelligent model Support vector regression Response surface method Accurate predictions 

Notes

References

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Computational Mathematics and Engineering, Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  3. 3.Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and BiotechnologyUniversity 20 Août 1955SkikdaAlgeria
  4. 4.Soil and Hydraulics Research Laboratory, Faculty of Engineering Sciences, Hydraulics DepartmentUniversity Badji-MokhtarAnnabaAlgeria
  5. 5.School of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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