Sea Surface Temperature Forecasting Using Machine Learning

• Author(s): Samta Kumari; Sajid Ali; Sushant Ranjan; Dr. Ishrat Ali; Prof. (Dr.) Sanjay Pachauri
• Paper ID: 1712141
• Page: 1341-1342
• Published Date: 18-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

Sea Surface Temperature (SST) is a critical climate variable that influences global weather, monsoon behavior, and marine ecosystems. Traditional numerical models struggle with high computational cost and nonlinear ocean?atmosphere dynamics. This work presents a machine-learning-based framework for SST forecasting using satellite observations and reanalysis data. Models including Random Forest, LSTM, and ConvLSTM are evaluated for short- and medium-term prediction. Results show that ML models significantly outperform persistence and statistical baselines in accuracy and efficiency. The study demonstrates the potential of data-driven methods to enhance operational SST forecasting and support climate monitoring applications.

Keywords

Sea Surface Temperature (SST); Machine Learning; Deep Learning; ConvLSTM; LSTM; Climate Forecasting; Oceanography; SatelliteData; Time-Series Prediction.

Citations

IRE Journals:
Samta Kumari, Sajid Ali, Sushant Ranjan, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri "Sea Surface Temperature Forecasting Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 1341-1342 https://doi.org/10.64388/IREV9I5-1712141

IEEE:
Samta Kumari, Sajid Ali, Sushant Ranjan, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri "Sea Surface Temperature Forecasting Using Machine Learning" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712141