Evaluation of Machine Learning Techniques for Hydrological Drought Modeling: A Case Study of the Wadi Ouahrane Basin in Algeria

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.