Assessing climate and land use impacts on surface water yield using remote sensing and machine learning
MAY 26, 2025
An ensemble of machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), were used to evaluate the effects of climate variability and land use on annual water yield. The study revealed significant land cover changes over a 30-year period. km2 (0.24%) to 41.57 km2 (10.38%).
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