Development of a knowledge-based noise test for robustness of vessel fuel consumption prediction models

Ping Chong Chua, Roland Chieng, Ruihan Wang, Kelvin Lee, Haiyan Xu, Xiuju Fu and Ran Yan

Maritime Policy & Management, 2025, vol. 52, issue 8, 1178-1207

Abstract: Machine learning techniques have been applied to vessel fuel consumption prediction with increasing availability of noon reports and sea/weather sensor data. However, such data often contain noise, raising concerns about model robustness. This study proposes a maritime knowledge–based noise addition approach, focusing on identified features of noon reports and sea/weather data. White, multiplicative, and systematic noise were introduced into features and tested on Domain Knowledge–based Artificial Neural Networks (DK-ANN) and Bi-directional Long Short-Term Memory (Bi-LSTM) models. Two key findings emerged: (1) vessel speed is the most sensitive feature to all noise types, exerting the greatest influence on model accuracy; (2) while noise degrades performance in both models, noise on wave period produces greater accuracy changes in Bi-LSTM compared to DK-ANN. This systematic evaluation not only helps identify the most robust models for real-world application but also pinpoints key parameters that significantly affect prediction stability. The proposed approach offers a practical guideline for testing robustness in future maritime prediction models, ensuring more reliable fuel consumption forecasts and supporting improved decision-making in maritime operations.

Date: 2025
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DOI: 10.1080/03088839.2025.2548786

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