Spatial risk assessment of maritime transportation in offshore waters of China using machine learning and geospatial big data

Maritime transportation plays a crucial role in global trade and economic development. However, this industry is exposed to various risks (e.g., natural disasters), which can cause significant economic and environmental damage. This study aims to develop a spatial risk assessment approach for maritime transportation using machine learning and geospatial big data to identify potential risks in China's maritime transportation industry. The proposed approach first produces risk maps that reveal significant variability in maritime transportation risks across different regions of China. Then, factor importance analysis identifies wave height, rainfall, and sea surface temperature as the most influential factors affecting navigational safety. Finally, capability indicators are employed to analyze the matching relationship between coastal search and rescue resources and maritime transportation risks. Our study provides valuable references for enhancing maritime emergency response capabilities and protecting marine ecological environments.

Automated summary

This study develops a spatial risk assessment approach for maritime transportation in China using machine learning and geospatial big data. The study identifies wave height, rainfall, and sea surface temperature as the most influential factors affecting navigational safety. It also analyzes the matching relationship between coastal search and rescue resources and maritime transportation risks.