Sensitivity analysis of coastal cities to effects of rainstorm and flood disasters

Heavy rains and floods cause human, material, and economic damage in cities worldwide. The severity of flooding has intensified due to accelerating urbanization. While much of the existing research on flood hazards emphasizes simulation and assessment, the correlation between indicators has yet to be explored. This study employs the Tree Gaussian Process sensitivity analysis method. Through rigorous sampling and correlation analysis, the model identifies critical determinants. Significantly, factors such as the water supply penetration rate (Var3), water pipeline density in built-up areas (Var4), centralized treatment rate of sewage treatment plants (Var6), agricultural land for forestry (Var13), and urban, village, and industrial and mining land (Var15) stand out as primary influencers on the flood-affected populace. These variables reflect a city's flood management capability and its dedication to resource stewardship and ecological equilibrium, underscoring its critical role in flood risk assessment and strategic mitigation. The study further illuminates that the interplay of these variables can exacerbate flood consequences, suggesting a compounded impact when variables operate in tandem. Recognizing these synergistic effects reveals a more pronounced flood threat than previously estimated, indicating that viewing these factors in silos might underrepresent the risk involved.