Integrating Support Vector Machines with Different Ensemble Learners for Improving Streamflow Simulation in an Ungauged Watershed

Streamflow simulation, particularly in ungauged watersheds, poses a significant challenge in surface water hydrology. The estimation of natural river and streamflow has been a research focus in recent years, with numerous strategies proposed. Hybrid ensemble soft computing models have proven their effectiveness in predicting flow rates. This study proposes a modeling approach that integrates a support vector machine (SVM) with several ensemble learning techniques, such as Bagging, Dagging, Random subspace, and Rotation Forest, to predict flow rates in natural rivers of a Mediterranean climate in Algeria. The gauging data of the hydrometric station “Amont des gorges” were used, and the following quantitative parameters were considered: flow, velocity, depth, width, and hydraulic radius. The proposed models were evaluated based on Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and correlation coefficient (R). Our results indicated that the ensemble models outperformed the standalone SVM model. More specifically, the SVM-Dagging model performed the best, with RMSE = 6.58, NSE = 0.76 and R = 0.96, followed by SVM-Bagging (RMSE = 6.83, NSE = 0.75, and R = 0.96), SVM-RF (RMSE = 6.95, NSE = 0.74, and R = 0.95), SVM-RSS (RMSE = 8.34, NSE = 0.62, and R = 0.93), and the standalone SVM models (RMSE = 7.71, NSE = 0.68, and R = 0.88), respectively. These findings suggest that the proposed ensemble models are valuable tools for accurately forecasting stream and river flows, aiding planners and decision-makers. Accurate prediction of flow rates in natural rivers can enhance water resource planning, optimize resource allocation, and improve water management practices.