Finding an accurate computational method for predicting pan evaporation (EP), 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 EP prediction on a monthly scale (EPm) 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 (R 2 )) 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 R 2 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 EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant and decreasing modes in EPm 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.
Rainfall and evaporation, which are known as two complex and unclear processes in hydrology, are among the key processes in the design and management of water resource projects. The application of artificial intelligence, in comparison with physical and empirical models, can be effective in the face of the complexity of hydrological processes. The present study was prepared with the aim of increasing the accuracy in monthly prediction of rainfall (R) and pan evaporation (EP) by providing a simple solution to determining new inputs for forecasting scenarios. Initially, the prediction of two parameters, R and EP, for the current and one–three lead times, by determining the different input modes, was developed with the SVM model. Then, in order to increase the accuracy of the predictions, the month number (τ) was added to all scenarios in predicting both the R and EP parameters. The results of the intelligent model using several statistical indices (i.e., root mean square error (RMSE), Kling–Gupta (KGE) and correlation coefficient (CC)), with the help of case visual indicators, were compared. The month number (τ) was able to greatly improve the prediction accuracy of both the R and EP parameters under the SVM model and overcome the complexities within these two hydrological processes that the scenarios were not initially able to solve with high accuracy. This is proven in all time steps. According to the RMSE, KGE and CC indices, the highest increase in the forecast accuracy for the upcoming two months of rainfall (Rt+2) for Ardabil station in scenario 2 (SVM-2) was 19.1, 858 and 125%, and for the current month of pan evaporation (EPt) for Urmia station in scenario 6 (SVM-6), this occurred at the rates of 40.2, 11.1 and 7.6%, respectively. Finally, in order to investigate the characteristic of the month number in the SVM model under special conditions such as considering the highest values of the R and EP time series, it was proved that by using the month number of the SVM model, again, the accuracy could be improved (on average, 17% improvement for rainfall, and 13% for pan evaporation) in almost all time steps. Due to the wide range of effects of the two variables studied in the hydrological discussion, the results of the present study can be useful in agricultural sciences and in water management in general and will help owners.
Finding an accurate computational method for predicting pan evaporation (EP), 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 EP prediction on a monthly scale (EPm) 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 EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant and decreasing modes in EPm 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.
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