Reference Evapotranspiration Estimation Using ANN, LSSVM, and M5 Tree Models (Case Study: of Babolsar and Ramsar Regions, Iran)

Abstract

Evapotranspiration is a non-linear and complex phenomenon requiring different climatic variables for accurate estimation. In this study, the performance of several artificial intelligence models in estimating the amount of monthly reference evapotranspiration was investigated. Babolsar and Ramsa regions located in the north of Iran were selected as case study models proposed in this study: artificial neural network (ANN), least square support vector machines (LSSVM), and M5 tree models. The data used in this study was gathered between 2009 till 2019 (11 consecutive years). In the present study, 70% of the data were used for the training stage, and 30% of the data were reserved for testing the proposed models. Models' performances were evaluated using several evaluation criteria, i.e., the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). The results for Babolsar and Ramsar stations showed that all three models have a relatively good performance in estimating the rate of reference evapotranspiration. However, the LSSVM model performed better than the other models. The R2, MAE, and RMSE for the LSSVM model in the test stage were 0.982, 0.366 mm, 0.425 mm, 0.937, 0.018 mm, and 0.350 mm for Babolsar and Ramsar stations, respectively.

Keywords

• reference evapotranspiration
• lssvm
• ann
• m5 tree
• babolsar
• ramsar