Random Forest Model for Agricultural and Hydrological Drought Analysis in Ethiopia (1982–2100)

This dataset provides the Python implementation, GEE workflow, and trained Random Forest models used for agricultural and hydrological drought assessment in Ethiopia from 1982–2100. The work integrates ERA5-Land, FLDAS, CHIRPS, CHIRTS, and multi-model CMIP6 datasets (SSP245, SSP585) to analyze past and future drought dynamics.
The Random Forest model used for prediction of agricultural (SSMI/based) and hydrological (SRI-based) drought indices is openly available for reuse and adaptation.