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| Created: | Jul 07, 2026 at 2:28 a.m. (UTC) | |
| Last updated: | Jul 07, 2026 at 10:09 p.m. (UTC) (Metadata update) | |
| Published date: | Jul 07, 2026 at 10:09 p.m. (UTC) | |
| DOI: | 10.4211/hs.ed81b766ae6a4c949b902935fc2ee05f | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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Abstract
The event-based runoff coefficient (ERC) is a pivotal hydrological metric that quantifies the relationship between rainfall and resultant runoff, offering valuable insights for effective water resource management and flood risk assessment. This study investigates the performance of Support Vector Regression (SVR) in predicting ERC and the impact of temporal and spatial controls on the ERC prediction. Utilizing data from the Ohio and Mid-Atlantic region, ERC predictions are derived from historical rainfall and runoff data using SVR. Results suggest that SVR demonstrates promising predictive accuracy, with model performance varying by temporal and spatial controls. While previous studies using models such as random forests, gradient-boosted decision trees (GBDT), or regression trees reported R² values up to 0.67, the present study attained R² values exceeding this benchmark for many watersheds, with the highest R² reaching 0.89. Temporally, watersheds that are predominantly influenced by climatic controls exhibit greater predictive difficulty compared to those governed by pre-event controls. Spatially, watersheds with extensive urbanization and higher elevations generally yield less accurate predictions, likely due to increased complexity in runoff dynamics. Additional research is needed to further refine the model’s adaptability to urban and mountainous environments to enhance predictive robustness across diverse landscapes.
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