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Current study presents the boosting of two base models, including a Heuristic Search Algorithm for finding the k shortest paths (K*) and an alternating model tree (AM Tree) through combining bagging (BA), dagging (DA), and random subspace (RS) hybridize models, to predict monthly suspended sediment load (SSL) in subtropical monsoon climatic regions. In this study, the rainfall (R), streamflow (Q), water level (W), and SSL with various lag-times were fed into the model as inputs. The study found that each input scenario has a unique ability to predict SSL. Different input scenario both manually and automatically derived to improve the modeling performance and reveal impact of the potential variable on the SSL modeling. Model fit outcomes finally were evaluated using graphical and statistical performance metrics. The most effective input scenario is the one that includes all possible input variables. The study also revealed that the hybridized ensemble models outperformed standalone model. The hybrid model of BA-K* performed superior to other models. While the standalone model of K* had the lowest prediction ability. Hybridized ensemble models assist in capturing extreme SSL values, especially the maximum SSL value. Results suggest that BA-K* model is superior to other models with R 2  = 0.80, RMSE = 772.94 Kg/s, PBIAS = −0.06, and NSE = 0.79. The reliability analysis also confirmed that the BA-K* model provided the optimal performance.

Suspended sediment load prediction using hybrid bagging-based heuristic search algorithm