Evaluating and mapping spatial drought in northeast Thailand: utilizing analytic hierarchy process and random forest algorithms

Abstract

Drought is a recurring and costly natural disaster that affects all climate zones, posing significant challenges to agriculture and livestock. This study evaluates drought vulnerabilities in Northeast Thailand using geospatial datasets and analysis combined with a random forest (RF) approach. The Analytical Hierarchy Process (AHP) was employed to map different drought types based on 14 spatial criteria. The RF model, trained using geospatial datasets and the Standardized Precipitation Index (SPI) from 96 meteorological stations, demonstrated high accuracy and reliability. The results revealed that approximately 70% of the region is vulnerable to moderate to extreme drought conditions. The AHP model achieved an overall accuracy (OA) of 85.19%, aligning closely with the RF model, which had an OA of 88.89%. The RF algorithm effectively mapped spatial drought events with high accuracy, as confirmed by comparisons with rainfall data. These findings provide essential insights for drought mitigation strategies and regional planning.