Prediction and uncertainty quantification of drought in North Benin

Abstract Drought forecasting is a critical tool for mitigating the severe impacts of water scarcity, particularly in regions like North Benin, where agriculture is a cornerstone of livelihoods. Despite the vital importance of its accurate prediction in resource management, the ability to quantify uncertainties in forecasts is a significant pain point to enable more informed and trustworthy decision-making. So, this study aims to develop an uncertainty-aware prediction model for drought forecasting in six key localities within the Alibori department—Banikoara, Gogounou, Kandi, Karimama, Malanville, and Segbana—each facing unique challenges due to drought. To achieve this, we conducted a comprehensive experiment involving six machine learning models (linear regression, ridge regression, random forest, Xgboost, LightGBM, and SVM) and four deep learning models (Conv1D, LSTM, GRU, and Conv1D-LSTM) using the Standardized Precipitation Index at a 6-month scale. To address the uncertainty quantification challenge, we employed the Ensemble Batch Prediction Interval, a conformal prediction method specifically designed for time series data. Our comparative analysis, framed within the Borda count methodology, utilized performance metrics such as R 2 , RMSE, MSE, and carbon footprint, as well as uncertainty quantification metrics, including empirical coverage and the width of prediction intervals. The top-performing models achieved $ {R}^2 $ scores of 98.29, 97.84, 97.76, 97.42, 96.61, and 97.07%, and prediction interval coverages of 0.94, 0.79, 0.93, 0.77, 0.73, and 0.93, respectively, for Banikoara, Gogounou, Malanville, Kandi, Segbana, and Karimama. The Conv1D-LSTM model stood out as the most effective, offering an optimal balance between predictive accuracy and uncertainty coverage.