Sea Surface Temperature Forecasting Using Machine Learning

@article{1712141,
      author = {Samta Kumari, Sajid Ali, Sushant Ranjan, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri},
      title = {Sea Surface Temperature Forecasting Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {1341-1342},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712141.pdf},
      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.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712141}
  }