Rainfall Prediction Using a Hybrid CNN-LSTM Method to Support Hydrometeorological Disaster Mitigation in Bima Regency

Abstract

Rainfall is one of the key meteorological parameters that significantly influences the occurrence of hydrometeorological disasters, such as floods and landslides, particularly in Bima Regency, which has a high level of vulnerability due to climate variability. Accurate rainfall prediction is essential for supporting decision-making in disaster mitigation and water resource management. This study aims to develop a rainfall prediction model using a Hybrid Convolutional Neural Network (CNN)–Long Short-Term Memory (LSTM) approach that effectively captures both the spatial and temporal characteristics of meteorological data. The dataset consists of historical rainfall records along with supporting meteorological variables collected from observation stations over a specified period. The research methodology includes data preprocessing, normalization using the Min-Max Scaling method, time series data construction, development of the Hybrid CNN–LSTM model, model training and testing, and performance evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The results indicate that the proposed Hybrid CNN–LSTM model provides accurate rainfall predictions by effectively extracting local features through CNN layers and capturing long-term temporal dependencies through LSTM layers. Furthermore, the proposed model demonstrates more stable performance in predicting rainfall fluctuations compared with conventional approaches, making it a promising solution for supporting early warning systems and decision-making in hydrometeorological disaster mitigation in Bima Regency. Therefore, the application of the Hybrid CNN–LSTM method offers an effective artificial intelligence-based approach to improving rainfall prediction accuracy.