Related Experiment Video
Updated: Aug 14, 2026
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Mapping Drought Vulnerability in the Chi River Basin, Thailand: A Machine Learning Framework Using H3 Hexagonal Grids
Narueset Prasertsri1, Patiwat Littidej1, Benjamabhorn Pumhirunroj2
1Department of Geoinformatics, Research Unit of Geoinformatics for Spatial Management, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
This study maps drought risk in Thailand
Area of Science:
- Environmental Science
- Remote Sensing
- Machine Learning
Background:
- Droughts are a recurring hazard in Thailand's Chi River Basin, causing agricultural losses.
- Low-lying floodplains do not prevent drought impacts.
Purpose of the Study:
- To develop a machine learning framework for village-relevant drought risk analysis.
- To integrate remote sensing data and static spatial variables for drought mapping.
Main Methods:
- Utilized Sentinel-2 indices (SMI, NDVI, MNDWI, VCI, NDMI) and spatial variables (DEM, TWI, prox2river, slope) aggregated to H3 hexagonal grids.
- Evaluated Random Forest, XGBoost, and LightGBM models using temporal and spatial validation.
- Employed SHAP analysis to determine variable importance.
Main Results:
- LightGBM model showed the best performance (AUC = 0.783 temporal, 0.714 spatial).
- Static topographic variables (76.1%) were more important than remote sensing indices (23.9%).
- Accuracy decreased for severe drought classes due to rare event data.
Conclusions:
- The framework serves as a drought risk mapping tool, identifying vulnerable areas.
- It is not an operational early warning system but is transferable to similar environments.
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