Drought Forecasting in Alibori Department in Benin using the Standardized Precipitation Index and Machine Learning Approaches

Abstract

Drought forecasting provides an early warning for the effective management of water resources to avoid or mitigate drought damage. In this study, the prediction of droughts is carried out in the department of Alibori in Benin republic using the standardized precipitation index (SPI) where two Machine Learning approaches were used to set up the drought prediction models which were Random Forest (RF) and Extreme Gradient Boosting (XGBOOST). The performance of these models was reported using metrics such as: coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and root mean absolute error (MAE). The results revealed that XGBOOST models gave better prediction performance for SPI 3, 6, 12 with coefficients of determination of 0.89, 0.83 and 0.99, respectively. The square root mean square error (RMSE) of the models gives 0.29, 0.40 and 0.07, respectively. This work demonstrated the potential of artificial intelligence approaches in the prediction of droughts in the Republic of Benin.