Drought is a recurring natural disaster that can cause significant damage
to agricultural production, human livelihoods, and the environment.
Drought forecasting is an important tool for managing and mitigating the
impacts of drought. This study aimed to improve drought forecasting
through the use of machine learning models. Specifically, the study
evaluated the performance of three machine learning models, namely
Extreme Learning Machine (ELM), Random Forest (RF), and Support
Vector Regression (SVR), for forecasting Standardized Precipitation
Index (SPI) drought. These models were trained using precipitation data
of Hiran region, Somalia from 1980 to 2021, to evaluate their ability to
accurately predict drought conditions. The results showed that the SVR
model performed the best, with an R2 value of 0.753, MAE of 0.344, and
RMSE of 0.488. The ELM and RF models also performed well. The study
highlights the potential of machine learning models to improve drought
forecasting, and the importance of evaluating multiple models to select
the one that performs best for a specific dataset.