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Predicting seaport disruptions from natural hazards using automated machine learning
Rafi Ullah Khan1, Jingbo Yin1, Mujtaba Asad2
1State Key Laboratory of Ocean Engineering, Department of Transportation Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Marine Pollution Bulletin
|July 25, 2025
Summary
Automated machine learning (AutoML) models can predict natural hazard-induced seaport disruptions. Neural networks achieved the highest accuracy, highlighting key factors like hazard severity and recovery efficiency for improved seaport resilience.
Area of Science:
- Maritime logistics and disaster management.
- Application of artificial intelligence in supply chain risk assessment.
Background:
- Seaports are critical for global trade, but natural hazards threaten their operations and economic stability.
- Predicting seaport disruptions from natural hazards is essential for ensuring supply chain continuity.
Purpose of the Study:
- To evaluate the effectiveness of automated machine learning (AutoML) models in predicting natural hazard-induced seaport disruptions.
- To identify the most accurate AutoML models and influential features for disruption prediction.
Main Methods:
- Utilized a dataset of seaport disruptions from 2010 to 2019.
- Evaluated 24 AutoML models across seven categories, including neural networks and support vector machines.
- Performed feature ranking to identify key factors influencing disruption prediction.
Main Results:
- Neural networks demonstrated the highest predictive accuracy (94.95%), with wide neural networks achieving 96.44%.
- Support vector machines showed the lowest accuracy (76.32%).
- Hazard "severity" and "recovery" were identified as the most influential factors.
Conclusions:
- AutoML models, particularly neural networks, show strong potential for predicting seaport disruptions.
- Findings provide insights for enhancing seaport resilience and preparedness against natural hazards.
- Future research should focus on incorporating more granular data and ensemble models for improved predictive capabilities.
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