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This study introduces a machine learning model to accurately identify ship operational phases using Automatic Identification System data. The model significantly improves emission estimates and aids in port management and emissions control.

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Area of Science:

  • Maritime technology
  • Environmental science
  • Machine learning applications

Background:

  • Ship engine and boiler work status critically impacts emission estimates, directly correlating with operational phases.
  • Accurate identification of ship operational phases is essential for precise emission calculations.
  • Automatic Identification System (AIS) data offers rich information on ship behavior.

Purpose of the Study:

  • To develop and validate a machine learning-based classification model for identifying ship operational phases.
  • To enhance the accuracy of ship emission estimates by improving operational phase identification.
  • To explore the utility of Automatic Identification System (AIS) data for this purpose.

Main Methods:

  • Extracted 12 features related to motion behavior and geospatial characteristics from AIS data of bulk carriers.
  • Developed and compared five machine learning models, with Random Forest (RF) showing superior performance.
  • Employed Progressive Ablation Feature Selection (PAFS) to optimize the feature set for the RF model.

Main Results:

  • The Random Forest model achieved high accuracy (96.66%), F1-score (93.34%), and AUC (99.93%) in identifying operational phases.
  • Reducing features from 12 to 8 using PAFS minimally impacted model performance, maintaining accuracy above 96%.
  • The RF model improved NOx emission estimation accuracy by 57.83% (main engine) and 93.89% (auxiliary engine) compared to traditional algorithms.

Conclusions:

  • The proposed machine learning approach effectively identifies ship operational phases, leading to more accurate emission estimates.
  • The model's validation on multiple bulk carriers demonstrates its robustness and applicability.
  • This approach offers significant benefits for port traffic management, ship emission control, and carbon tax prediction.