In this study, the performance of SWAT hydrological model and three computational intelligence methods used to simulate river flow are investigated. After collecting the data required for all models used, the calibration and validation stages were performed. Using the SWAT model and three methods of the Extreme Machine Learning (EML), the Support Vector Regression (SVR), and the Least Squares Support Vector Regression (LSSVR), Maharlu Lake Basin stream flow was simulated and the results obtained at Shiraz station were used for this study. A noise reduction filter was employed to improve the results from the computational intelligence methods, and SUFI-2 algorithm was used to analyze the uncertainty of the SWAT model. Finally, in order to evaluate the models developed and the SWAT model, three statistics (RMSE), (R²), and (NS) coefficient were used. The results indicated that the SWAT model and the machine learning models were generally appropriate tools for daily flow modeling, but the LSSVR model showed less errors in both learning and testing, with the coefficients NS = 0.997 and R² = 0.997 in the calibration stage and NS = 0.994 and R² = 0.994 in the validation stage, which prove their better performance compared to the other methods and the SWAT model.

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In recent years, the use of climatic databases and satellite products by researchers has become increasingly common in the field of climate modeling and research. These datasets play an important role in developing countries. This study evaluated two reanalyses, CMORPH and SM2RAIN-ASCAT over Maharlu Lake, a semi-arid region in Iran. The results showed that these two near-time datasets do not have accurate data over this basin. However, the Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ratio (FAR) statistics showed acceptable accuracy in the detection of precipitation. The coefficient of determination and root mean square error statistics have unacceptable accuracy over this area. The monthly changes in each of the indices showed that the CMORPH database had more errors in the spring months, but in other months the error rate was improved. SM2RAIN-ASCAT had better accuracy over this area relative to CMORPH. The estimation of the total accuracy of the data showed that these two satellite databases were not capable of estimating precipitation in the area.

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