Assessing and Optimizing Urban Dynamic Resilience to Extreme Rainfall from Shock to Recovery

Abstract

Climate change has intensified extreme rainfall events, challenging progress toward SDG 11’s urban resilience targets. Current assessment methods often neglect dynamic recovery processes and regional precipitation disparities. We propose a three-phase framework combining interpretable machine learning (ML) and factorial experiments, using the Prep_shock index that integrates standardized rainfall intensity, capital exposure, and historical probability, to evaluate the dynamic resilience of 220+ Chinese cities from 2019 to 2022. Key findings reveal that: (1) The Prep_shock index effectively eliminates north-south precipitation biases, identifying Shandong coastal cities and Yangtze River Delta city clusters (36.2%) as high-resilience areas, in contrast to Henan Province. COVID-19 exacerbated systemic risks in megacities, undermining their capital protection capacities. (2) Spatial diagnostics classify 75.6% of the cities into Quadrant III (the balanced resilience category), with recovery times decreasing from the west to the east. Super-large cities like Zhengzhou (2021) exhibited critical recovery deficiencies (Quadrant IV). (3) Interpretable ML models (XGBoost/EBM) identify redundancy as the dominant resilience driver—robustness governs baseline resilience, while recovery relies on emergency support (for example, hospital beds density and fiscal inputs) and redundant infrastructure (for example, road network density). (4) Factorial experiments reveal optimization trade-offs: simultaneous enhancement of rapidity and redundancy diminishes their individual benefits, necessitating context-specific prioritization. The study advances dynamic resilience assessment methods and proposes quadrant-specific strategies for tailored urban adaptation.

Keywords

Dynamic assessment / Extreme rainfall / Factorial analysis / Machine learning / Resilience optimization / Urban resilience

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