Abstract Sediment yield prediction is vital for sustainable watershed management, particularly in data-scarce regions. This study, conducted in the Göksun Çayı Karaahmet sub-basin, Türkiye, evaluated whether sediment connectivity indices can reproduce outputs from the Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE). Sediment yield was modeled for 196 sub-catchments and 69 rainfall events over 10 years using GIS-based factors: rainfall erosivity, soil erodibility, slope length-steepness, land cover, and hydrological parameters. Despite different assumptions (rainfall erosivity versus runoff and peak discharge), RUSLE and MUSLE showed strong agreement (R² = 0.87 at the event scale; R² = 0.93 at the sub-catchment scale). Predicted sediment yields ranged from 0.02 to 16.46 t ha -1 (MUSLE) and 0.04–10.63 t ha -1 (RUSLE/SDR), with mean values of 0.89 and 0.96 t ha -1 , respectively. Sediment connectivity indices-including the Index of Connectivity (IC), Sediment Delivery Ratio (SDR), and Topographic Wetness Index (TWI), were applied as inputs to five machine learning (ML) models (XGBoost, Random Forest, k-NN, SVR, and ANN). XGBoost and Random Forest achieved the best performance (R² = 0.912–0.942, RMSE = 0.065–0.089, MAE = 0.047–0.055), reproducing empirical outputs. IC, SDR, and TWI were dominant predictors. These results demonstrate that connectivity metrics integrated with ML can emulate empirical erosion models, offering a scalable, data-efficient alternative for ungauged basins. However, because the models were trained on RUSLE/MUSLE outputs from 69 events under static land use and climate, they may underpredict extreme sediment events and require field validation before operational use.
Mohammadi et al. (Tue,) studied this question.