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Runoff simulation based on landscape pattern classification and machine learning
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DOI：10.1016/j.ejrh.2025.102968.png)
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Abstract
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• A classification-based runoff simulation framework was developed integrating landscape patterns and machine learning. • A baseline meteorology-only model confirmed the added value of landscape classification. • The coupled landscape pattern XGBoost runoff model achieved the highest simulation accuracy and the strongest robustness. • SHAP analysis revealed key variables and threshold effects across different basin types. • The framework showed strong spatial transferability and practical watershed management value.
Keywords:
Landscape pattern
Spatial structure
Machine learning
Runoff
Middle Yellow River
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