Mapping Drought Vulnerability in the Chi River Basin, Thailand: A Machine Learning Framework Using H3 Hexagonal Grids and Topographic Variables

Drought is a recurrent hazard in the Chi River Basin, northeastern Thailand, causing agricultural losses despite its low-lying floodplain setting. This study developed a machine learning framework integrating Sentinel-2 indices (SMI, NDVI, MNDWI, VCI, NDMI) and static spatial variables (DEM, TWI, prox2river, slope) aggregated to H3 hexagonal grids (≈5.96 km2) for village-relevant analysis. Drought reports from 1482 observations (2019–2024) were aggregated to 203 grid cells. Three models—Random Forest, XGBoost, and LightGBM—were evaluated using temporal (training: 2019–2023; test: 2024) and spatial holdout validation. LightGBM achieved the best performance with AUC = 0.783 (temporal) and 0.714 (spatial), accuracy = 78.3%, and balanced accuracy = 76.4%. Five-class severity classification showed declining accuracy from 71.4% (Very Low) to 25.0% (Severe), limited by rare event sample sizes. SHAP analysis revealed static topographic variables dominated importance (76.1%) over remote sensing indices (23.9%), with weak individual correlations (|r| < 0.10). The framework is best characterized as a drought risk mapping tool for identifying persistently vulnerable areas rather than an operational early warning system. The methodology is transferable to similar floodplain environments with local re-estimation and validation.