Comparison of Sediment Rating Curve Developed by Using KNN, SVM and Random Forest Method for Gandaki River Basin, Nepal - Katwal, Kamal; Thapa, Sudeep; Bajracharya, Dipendra; Ranjit, Gaurab; Karna, Isha; Pudasaini, Bishal (International Journal on Engineering Technology, 2025)
Accurate estimation of suspended sediment load (SSL) is crucial for effective water resource management in Nepal's monsoon-influenced Himalayan River systems, where traditional sediment rating curves (SRCs) often fail to capture complex, non-linear discharge-sediment relationships. This study developed season-specific sediment rating curves for Station 430.5 on the Seti-Gandaki River using three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) to address the limitations of conventional power-law approaches across Nepal's four distinct hydrological seasons. Daily discharge and suspended sediment concentration data from 2004-2017 were analyzed to characterize seasonal variability, with monsoon periods exhibiting peak flows exceeding 1,400 m³/s and sediment concentrations reaching over 20,000 ppm, contrasting sharply with winter baseflow conditions below 35 m³/s and sediment concentrations under 250 ppm. Model performance was evaluated using coefficient of determination (R²) and Mean Absolute Percentage Error (MAPE), revealing optimal algorithm selection varied significantly across seasons: KNN demonstrated superior accuracy for monsoon (R² = 0.22) and post-monsoon (R² = 0.97) periods, while SVM showed better performance during winter (R² = 0.81) conditions, though all models struggled with pre-monsoon transitional dynamics (R² < 0.25). The resulting power-law equations—S = 0.85 × Q^1.81 (pre-monsoon), S = 333.66 × Q^0.31 (monsoon), S = 26.55 × Q^0.62 (post-monsoon), and S = 17.61 × Q^0.72 (winter) captured distinct seasonal sediment transport mechanisms, with monsoon conditions presenting the greatest modeling challenges (MAPE = 90.67%) due to complex hysteresis effects, variable sediment availability, and extreme non-linearity that conventional machine learning approaches cannot fully represent. While machine learning models demonstrated substantial improvements over traditional SRCs, particularly during stable post-monsoon and winter periods, the persistent high prediction errors during monsoon conditions highlight the need for advanced deep learning architectures capable of capturing temporal dependencies and multi-factorial sediment transport controls. These findings provide essential foundations for improving sediment load estimation in data-scarce Himalayan basins and inform sustainable water resource management, hydropower planning, and climate adaptation strategies in rapidly changing mountain river systems.