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Machine Learning Models for Predicting Sea Surface Temperature Variability and Trends
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I10-1716530
Abstract
Sea Surface Temperature (SST) is an important factor in understanding climate systems, weather patterns, and marine environments. Accurate prediction of SST helps in forecasting events like cyclones, monsoons, and climate change impacts. Traditional methods often fail to capture complex and nonlinear patterns present in SST data. In this research, machine learning models such as Linear Regression, Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) are used to predict SST variability and trends. Historical SST datasets from satellite and climate sources are used for training and testing the models. The performance of each model is evaluated using standard metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² score. The results show that LSTM performs better than other models due to its ability to learn time-based patterns effectively. This study proves that machine learning can significantly improve SST prediction accuracy and can be useful for climate-related applications.
Keywords
Sea Surface Temperature (SST); Machine Learning; Deep Learning; Conv LSTM; LSTM; Climate Forecasting; Oceanography; Satellite Data; Time-Series Prediction.
How to cite this paper
@article{1716530,
author = {Sajid Ali, Samta Kumari, Tajendra Riya, Prof. (Dr.) Sanjay Pachauri},
title = {Machine Learning Models for Predicting Sea Surface Temperature Variability and Trends},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {2309-2313},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716530.pdf},
abstract = {Sea Surface Temperature (SST) is an important factor in understanding climate systems, weather patterns, and marine environments. Accurate prediction of SST helps in forecasting events like cyclones, monsoons, and climate change impacts. Traditional methods often fail to capture complex and nonlinear patterns present in SST data. In this research, machine learning models such as Linear Regression, Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) are used to predict SST variability and trends. Historical SST datasets from satellite and climate sources are used for training and testing the models. The performance of each model is evaluated using standard metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² score. The results show that LSTM performs better than other models due to its ability to learn time-based patterns effectively. This study proves that machine learning can significantly improve SST prediction accuracy and can be useful for climate-related applications.},
keywords = {Sea Surface Temperature (SST); Machine Learning; Deep Learning; Conv LSTM; LSTM; Climate Forecasting; Oceanography; Satellite Data; Time-Series Prediction.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716530}
}