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Sensor Fusion-Based Machine Learning Algorithms for Meteorological Conditions Nowcasting in Port Scenarios.

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Accurate short-term wind forecasting for ports is crucial. This study developed a machine learning framework using sensor fusion for real-time wind nowcasting, with XGBoost showing superior performance.

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Area of Science:

  • Maritime operations and environmental monitoring
  • Machine learning applications in logistics
  • Internet of Things (IoT) for port management

Background:

  • Port operations are increasingly impacted by unpredictable weather and environmental shifts.
  • Accurate short-term forecasting is essential for maritime safety and efficiency.
  • Existing systems require enhanced situational awareness for critical operations.

Purpose of the Study:

  • To develop a real-time, multi-target nowcasting framework for wind conditions.
  • To integrate heterogeneous data sources using sensor fusion and IoT architecture.
  • To evaluate machine learning models for predicting wind gust speed, sustained wind speed, and direction.

Main Methods:

  • Utilized an IoT architecture (oneM2M standard) at the Port of Livorno.
  • Integrated data from meteorological stations, anemometers, and vessel-mounted LiDAR.
  • Employed feature-level sensor fusion and compared Random Forest, XGBoost, LSTM, TCN, Ensemble Neural Network, Transformer, and Kalman filter models.

Main Results:

  • XGBoost demonstrated the highest accuracy for all wind targets (R² ≈ 0.999 in single-split, mean R² = 0.9976 in cross-validation).
  • Ensemble models showed improved robustness compared to deep learning methods.
  • The sensor fusion framework effectively enhanced situational awareness for critical variables like gust speed.

Conclusions:

  • The proposed sensor fusion-based machine learning framework is highly effective for real-time wind nowcasting.
  • XGBoost is a top-performing model for this multi-target prediction task.
  • The framework has significant potential for deployment in Maritime Autonomous Surface Ship (MASS) systems and port decision-support platforms for enhanced safety and operational continuity.