ENSO dataset & comparison of deep learning models for ENSO forecasting

Mir, Shabana; Arbab, Masood Ahmad; Rehman, Sadaqat ur

Abstract

Forecasting the El Nino-Southern Oscillation (ENSO) is a challenging task in climatology. It is one of the main factors responsible for the Earth’s interannual climatic fluctuation and can result in many climatic anomalies. The impacts include natural disasters (floods, droughts), low & high agriculture yields, price fluctuation, energy demand, availability of water resources, animal movement, and many more. This study presents a comprehensive ENSO dataset containing standard indicators and other relevant data to facilitate ENSO analysis and forecasting. To ensure the dataset's validity and reliability, we performed extensive data analysis and trained four basic deep models for time series forecasting (i.e. CNN, RNN, LSTM, and hybrids). The data analysis confirmed the accuracy and suitability of the dataset for ENSO forecasting. The LSTM model achieved the best fit to the data, leading to superior performance in forecasting ENSO events.

Citation

Mir, S., Arbab, M. A., & Rehman, S. U. (2024). ENSO dataset & comparison of deep learning models for ENSO forecasting. Earth Science Informatics, 17(3), 2623-2628. https://doi.org/10.1007/s12145-024-01295-6

• Journal Article Type: Article
• Acceptance Date: Mar 22, 2024
• Online Publication Date: Apr 10, 2024
• Publication Date: Jun 1, 2024
• Deposit Date: Jun 7, 2024
• Publicly Available Date: Jun 7, 2024
• Journal: Earth Science Informatics
• Print ISSN: 1865-0473
• Electronic ISSN: 1865-0481
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 3
• Pages: 2623-2628
• DOI: https://doi.org/10.1007/s12145-024-01295-6
• Keywords: El Nino-Southern Oscillation (ENSO), Time series forecasting, Climate changes, Time series analysis, Deep learning, Neural networks, El Nino La Nina prediction