Intelligent daily rainfall prediction for early warning using deep learning and satellite data: application to Bouaflé and Zuénoula stations, Ivory coast

Abstract. Recurrent flooding in the Marahoué region, particularly in Bouaflé and Zuénoula, underscores the need for reliable and operational tools to anticipate hydrological risks and support early warning systems. This study presents a rainfall forecasting framework based on Long Short-Term Memory (LSTM) neural networks, integrating satellite-derived precipitation products and reanalysis-based atmospheric variables to predict daily rainfall at the Bouaflé and Zuénoula stations at t + 1, t + 3, and t + 7 d lead times. The performance of the LSTM model was systematically evaluated and compared with commonly used reference models, namely Random Forest (RF), Extra Trees (ET), and XGBoost (XGB), using standard statistical metrics (R2, NSE, Pearson correlation coefficient R, normalized RMSE, and MAE). The results show that the LSTM model consistently outperforms the reference models across all forecasting horizons and at both study stations. At short and medium lead times (t + 1 and t + 3), LSTM exhibits strong predictive skill, with R2 and NSE values exceeding 90 %, indicating an accurate representation of daily rainfall variability. Although performance decreases at the seven-day horizon due to increasing uncertainty and challenges in capturing extreme events, LSTM remains more robust than tree-based models, whose accuracy degrades markedly with increasing lead time. These findings confirm the relevance of LSTM-based approaches for rainfall forecasting and early warning applications in flood-prone regions. Future work will focus on integrating additional atmospheric predictors and applying advanced hyperparameter optimization techniques to improve long-term forecast reliability.