Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data

Abstract

In this study, the ability of four different data-driven methods, multilayer perceptron artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC) and gene expression programming (GEP), was investigated in predicting long-term monthly reference evapotranspiration (ET0) by using data from 50 stations in Iran. The periodicity component, station latitude, longitude and altitude values were used as inputs to the applied models to predict the long-term monthly ET0 values. The overall accuracies of the multilayer perceptron ANN, ANFIS-GP and ANFIS-SC models were found to be similar to each other. The GEP model provided the worst estimates. The maximum determination coefficient (R2) values were found to be 0.997, 998 and 0.994 for the ANN, ANFIS-GP and ANFIS-SC models in Karaj station, respectively. The highest R2 value (0.978) of GEP model was found for the Qom station. The minimum R2 values were respectively found as 0.959 and 0.935 for the ANN and ANFIS-GP models in Bandar Abbas station while the ANFIS-SC and GEP models gave the minimum R2 values of 0.937 and 0.677 in the Tabriz and Kerman stations, respectively. The results indicated that the long-term monthly reference evapotranspiration of any site can be successfully estimated by data-driven methods applied in this study without climatic measurements. The interpolated maps of ET0 were also obtained by using the optimal ANFIS-GP model and evaluated in the study. The ET0 maps showed that the highest amounts of reference evapotranspiration occurred in the southern and especially southeastern parts of the Iran.