Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

Abstract

<title>Abstract</title> <p>Finding an accurate computational method for predicting pan evaporation (<italic>EP</italic>), can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called Multiple Model-Support Vector Machine (MM-SVM) with the aim of increasing the accuracy of <italic>EP</italic> prediction on a monthly scale (<italic>EP</italic>_{<italic>m</italic>}) in two meteorological stations (Ardabil and Khalkhal) using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)) were evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE) and coefficient of determination (R²)) and with the help of case visual indicators Were compared. According to the results of evaluation indicators in the test phase, two models MM-SVM-6 and ANN-5 with (RMSE, MAE, KGE and R² equal to 1.088, 0.761, 0.79, 0.54 mm. month^{− 1}, 0.819, 0.903 and 0.939, 0.962) and with three input variables, were introduced as the top models in Ardabil and Khalkhal stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs predicted by independent models, its power to predict <italic>EP</italic>_{<italic>m</italic>} in scenarios where there is a high correlation between its components with <italic>EP</italic>_{<italic>m</italic>}, in a feasible state Accept to show. So that the incremental, constant and decreasing modes in <italic>EP</italic>_{<italic>m</italic>} prediction accuracy by this hybrid model under the above conditions (especially in Ardabil station) were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.</p>