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Hybrid systems using residual modeling for sea surface temperature forecasting.

Paulo S G de Mattos Neto1, George D C Cavalcanti2, Domingos S de O Santos Júnior2

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Hybrid systems combining machine learning models improve sea surface temperature (SST) forecasting accuracy. These novel approaches outperform traditional statistical and single machine learning models for predicting crucial climate and weather patterns.

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Area of Science:

  • Environmental Science
  • Climate Science
  • Data Science

Background:

  • Sea surface temperature (SST) is a critical environmental indicator for global climate and weather patterns.
  • Accurate SST forecasting is vital for governmental and environmental decision-making.
  • Traditional statistical models and single machine learning (ML) models face challenges in SST time series modeling due to parameter tuning complexities, leading to potential misspecification, overfitting, or underfitting.

Purpose of the Study:

  • To propose and evaluate hybrid systems (HS) that combine ML models for enhanced SST forecasting performance.
  • To investigate the effectiveness of residual forecasting as a method for improving SST predictions.
  • To compare the performance of different HS configurations against single ML models and existing literature approaches.

Main Methods:

  • Developed hybrid systems combining two ML models: Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) networks.
  • Employed residual forecasting to integrate the ML models within the hybrid systems.
  • Conducted experimental evaluations on three Atlantic Ocean SST datasets using Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) as performance metrics.

Main Results:

  • The best SVR-based HS improved MSE by [Formula: see text], [Formula: see text], and [Formula: see text] across the analyzed datasets compared to the single SVR model.
  • The LSTM-based HS demonstrated improvements of [Formula: see text], [Formula: see text], and [Formula: see text] over the single LSTM model.
  • At least one HS configuration achieved higher accuracy than existing statistical and ML models in all evaluated cases, with nonlinear combinations yielding the best performance.

Conclusions:

  • Hybrid systems integrating ML models via residual forecasting offer a superior alternative for SST time series modeling.
  • The proposed HS effectively address limitations of single ML models, leading to more accurate and reliable SST forecasts.
  • Nonlinear combinations of ML models within hybrid systems represent the most promising approach for advancing SST forecasting capabilities.