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| Format: | Recurso digital |
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Zenodo
2025
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| Online-Zugang: | https://doi.org/10.5281/zenodo.18765917 |
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Inhaltsangabe:
- <p>Sediment transport in Himalayan rivers is highly dynamic, driven by intense monsoon rainfall, steep topography,<br>and fragile geology, posing challenges for water resource management and infrastructure sustainability. This study<br>develops season-specific sediment rating curves (SRCs) for Station 120 in the Mahakali River Basin, Nepal, using<br>machine learning (ML) models to improve sediment load estimation under varying hydrological conditions. Daily<br>discharge and suspended sediment data from 2007 to 2014 were analyzed across four seasons Pre-Monsoon,<br>Monsoon, Post-Monsoon, and Winter accounting for seasonal variability in sediment transport dynamics. Three ML<br>models K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) were evaluated, and<br>the best-performing model for each season was selected based on R² and Mean Absolute Percentage Error (MAPE).<br>SVM outperformed others in Pre-Monsoon, Monsoon, and Winter seasons, while RF showed superior accuracy in Post-<br>Monsoon. Power-law SRCs were derived from predicted sediment concentrations, yielding equations: S=4.28×Q1.16<br>(Monsoon), S=1.21×Q1.19 (Post-Monsoon), S=3.81×Q1.17 (Pre-Monsoon), and S=826.88×Q −0.71 (Winter). Despite<br>improved accuracy, higher MAPE during the Monsoon season highlights the limitations of ML models in capturing<br>extreme events. The findings support the need for advanced deep learning approaches, as suggested by prior studies,<br>to better represent non-linear and time-dependent sediment processes. This research provides a robust, seasonally<br>adaptive framework for sediment load estimation in data-scarce Himalayan basins, supporting improved sediment<br>management.</p>