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Planting vegetation is one of the practical solutions for reducing sedimenttransfer rates. Increasing vegetation cover decreases environmental pollutionand sediment transport rate (STR). Since sediments and vegetation interactcomplexly, predicting sediment transport rates is challenging. This study aimsto predict sediment transport rate under vegetation cover using new andoptimized versions of the group method of data handling (GMDH). Additionally,this study introduces a new ensemble model for predicting sediment transportrates. Model inputs include wave height, wave velocity, density cover, waveforce, D50, the height of vegetation cover, and cover stem diameter. Astandalone GMDH model and optimized GMDH models, including GMDH honey badgeralgorithm (HBA) GMDH rat swarm algorithm (RSOA)vGMDH sine cosine algorithm(SCA), and GMDH particle swarm optimization (GMDH-PSO), were used to predictsediment transport rates. As the next step, the outputs of standalone andoptimized GMDH were used to construct an ensemble model. The MAE of theensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA,GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash Sutcliffe coefficient(NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDHwere 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, thisstudy demonstrated that vegetation cover decreased sediment transport rate by90 percent. The results indicated that the ensemble and GMDH-HBA models couldaccurately predict sediment transport rates. Based on the results of thisstudy, sediment transport rate can be monitored using the IMM and GMDH-HBA.These results are useful for managing and planning water resources in largebasins.

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover