A probabilistic GIS-based framework for urban flood risk assessment in Chengdu metro network

Area of Science:

• Urban planning
• Environmental science
• Civil engineering

Background:

• Urban metro networks are critically vulnerable to flooding.
• Existing flood risk assessments often use deterministic methods, failing to adequately address uncertainty.
• This limitation hinders effective urban flood risk management for critical infrastructure.

Purpose of the Study:

• To develop a probabilistic GIS-based flood risk assessment framework for urban metro networks.
• To explicitly quantify uncertainty in expert judgments and incorporate a comprehensive indicator system.
• To provide spatially explicit risk mapping for both operational and planned metro infrastructure.

Main Methods:

• Integration of the Analytic Hierarchy Process (AHP) with its Monte Carlo extension (MC-AHP).
• Utilization of β-PERT simulations to quantify uncertainty in expert judgments.
• Inclusion of hazard, exposure, and vulnerability indicators, including passenger flow and under-construction lines.

Main Results:

• A concentric flood risk distribution was identified in the Chengdu metro network, with the urban core showing high vulnerability.
• The MC-AHP method identified more high-risk areas and reduced subjective bias compared to traditional AHP.
• The framework demonstrated predictive reliability when validated against historical flood events.

Conclusions:

• The probabilistic GIS framework advances urban flood risk assessment methodology by incorporating uncertainty.
• The study provides generalizable insights for enhancing metro network resilience globally.
• Addressing climate change and urbanization requires robust, uncertainty-aware flood risk assessments for urban infrastructure.