Assessing the Robustness of SWAT Model and Machine Learning Techniques in Predicting Extreme Streamflow Events. A Case Study of Astor Basin, Pakistan

 Forecasting stream flow accurately is essential for managing water resources, preventing flooding, and designing the environment for intricate watersheds. This study conducts a comprehensive assessment of streamflow simulation models—Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Support Vector Machine (SVM), and the Soil and Water Assessment Tool (SWAT) hydrological model—covering the period from 1985 to 2018 in the Astore Basin, Pakistan. The GMRC-WAPDA and SWHP-WAPDA provided daily streamflow data from the Doyian gauging station in the Astor River Basin, as well as meteorological data collected from two automatic weather stations (AWS) located at Rama, Rattu and Astor. A total number of four soil classes (lithosols, calcaric, gleysols and fluvisol) was observed in the basin. The primary objective was to comprehensively assess the predictive performances of these models across distinct time segments and gauge their reliability in simulating streamflow dynamics. The study commenced with examining the SWAT model's performance, utilizing the NSE, PBIAS, R², and RMSE metrics during calibration (1985–2000) and validation (2001–2009) periods. While the SWAT model effectively estimated streamflow, it exhibited limitations in accurately predicting peak and low-flow conditions. Subsequently, the machine learning models (MLP, ELM, and SVM) were scrutinized concerning their performance metrics—R², NSE, PBIAS, and RMSE—across training (1985–1995), validation (1996–2005), and testing (2005–2009) datasets. ELM displayed superior performance during the training phase, boasting a remarkable R² of 0.94, followed by SVM and MLP. MLP showcased consistent strength in validation, maintaining an R² of 0.73, while SVM followed with an R² of 0.71. Despite their merits, none of the models precisely replicated observed streamflow patterns, as evidenced by the discrepancies between the observed flow and the SWAT model's simulations. This emphasizes the necessity for ongoing refinement and validation to enhance predictive accuracy and ensure closer alignment with real-world hydrological dynamics. This extensive comparative analysis offers critical insights into the nuances of MLP, ELM, SVM, and SWAT model performances, highlighting their varied strengths and limitations across distinct temporal segments. It underscores the importance of continual refinement and validation to improve predictive capabilities, which is essential for accurate streamflow simulations and effective water resource management in the Astor Basin and similar hydrological contexts.