Hybrid deep learning for probabilistic rainfall forecasting in a changing climate

This paper proposes a hybrid deep learning framework for predicting monthly rainfall in Latakia, Syria, using historic data from 1993 to 2023 and utilizing three deep learning architectures: Convolutional Neural Network (CNN), Convolutional Long Short-Term Memory network (ConvLSTM), and CNN–Fourier. For that purpose, the rainfall data was preprocessed and normalization techniques were applied to ensure model convergence. Each model was then trained on a partitioned dataset (training, validation, and testing) and optimized through early stopping and regularization. It was noted that CNN model captured underlying rainfall patterns but was less robust in predicting extreme rainfall events. On the other hand, the ConvLSTM model accurately predicted upper-bound variability during periods of heavy rainfall. The CNN-Fourier model incorporated frequency domain features to reduce noise and enhance forecast stability. A statistical comparison of the models showed that CNN-Fourier demonstrates the effectiveness of combining Fourier noise removal and deep learning to address rainfall variability through hybridization. To inform environmental planning, the forecasts were extended to 2027 using three scenarios: minimum, average, and maximum. These scenarios may offer valuable insight for stakeholders in preparing for evolving climatic conditions by incorporating spectral filtering and hybridizing spatio-temporal modeling within a robust statistical framework.