GIS-integrated flood risk assessment for metro systems based on bayesian cosine maximization method: a case study in...

Area of Science:

• Environmental Science
• Urban Planning
• Civil Engineering

Background:

• Extreme rainfall events are increasing due to climate change, exacerbating urban flooding.
• Urban underground rail transit systems face significant operational safety threats from flooding.
• Accurate spatial flood risk assessment is crucial for metro system resilience.

Purpose of the Study:

• To develop a comprehensive flood risk assessment model for metro systems.
• To enhance objectivity and spatial accuracy in flood risk evaluation.
• To apply the model to the Beijing metro system for case study analysis.

Main Methods:

• Integration of the Bayesian cosine maximization method (BCMM) with Geographic Information System (GIS) technology.
• Development of a 13-indicator evaluation system covering hazard, exposure, and vulnerability.
• Spatial quantification of multisource data and optimization of indicator weights using Bayesian theory and cosine maximization.

Main Results:

• Central Beijing and Tongzhou Districts identified as extremely high-risk flood areas (4.89% of total area).
• Within the Beijing metro system, 12.45% is extremely high-risk and 21.97% is high-risk.
• BCMM demonstrated improved consistency and accuracy compared to the traditional fuzzy analytic hierarchy process (FAHP), reducing weight calculation Euclidean distance by 37.5%.

Conclusions:

• The BCMM-GIS model provides a more objective and spatially accurate flood risk assessment for metro systems.
• Significant portions of the Beijing metro system are vulnerable to extreme rainfall flooding.
• The model highlights the need for enhanced flood mitigation strategies for urban underground infrastructure.