[2604.04998] El Nino Prediction Based on Weather Forecast and Geographical Time-series Data

Computer Science > Machine Learning arXiv:2604.04998 (cs) [Submitted on 5 Apr 2026] Title:El Nino Prediction Based on Weather Forecast and Geographical Time-series Data Authors:Viet Trinh, Ha-Vy Luu, Quoc-Khiem Nguyen-Pham, Hung Tong, Thanh-Huyen Tran, Hoai-Nam Nguyen Dang View a PDF of the paper titled El Nino Prediction Based on Weather Forecast and Geographical Time-series Data, by Viet Trinh and 5 other authors View PDF HTML (experimental) Abstract:This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Niño events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic and atmospheric indices, which may lack the granularity or dynamic interplay captured by comprehensive meteorological and geographical datasets. Our framework integrates real-time global weather forecast data with anomalies, subsurface ocean heat content, and atmospheric pressure across various temporal and spatial resolutions. Leveraging a hybrid deep learning architecture that combines a Convolutional Neural Network (CNN) for spatial feature extraction and a Long Short-Term Memory (LSTM) network for temporal dependency modeling, the framework aims to identify complex precursors and evolving patterns of El Niño events. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2604.04998 [cs.LG]   (or arXiv:2604.04998v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.04998 Focus to ...