Comparative Analysis of Deep Learning Algorithms for Predicting ENSO Based on Non-sequential Sampling Procedure Algorithms - Faulina, Ria; Hasanah, Siti Hadijah; Nuramaliyah, Nuramaliyah; Fitriana, Ika Nur Laily (Knowledge Engineering and Data Science, 2025)
The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN and LSTM models in predictive accuracy and robustness. Specifically, the proposed model achieved the lowest Mean Absolute Error (MAE) of 13.97 and Root Mean Square Error (RMSE) of 15.76 across multiple test samples. These findings indicate that the integration of convolution-based feature extraction and sequential memory learning effectively captures complex ENSO temporal patterns. The proposed approach offers a reliable computational framework for climate forecasting and may contribute to improved anticipatory planning in climate-sensitive decision-making contexts.