Public-service exposure-oriented coastal flood susceptibility assessment and priority zone identification in the Pearl River Estuary

The increasing vulnerability of coastal communities to flooding, exacerbated by rapid urbanization, demands a shift in how we assess and respond to this escalating threat. Traditional coastal flood susceptibility mapping, while valuable, often lacks the critical dimension of understanding *what* is at risk beyond just land area. This new research, focusing on the Pearl River Estuary, addresses this gap by integrating flood susceptibility assessment with an analysis of exposure to vital public services. This approach resonates with our ongoing coverage of how digital infrastructure is transforming resource management – as demonstrated by Does new digital infrastructure promote the low-carbon transformation of fisheries, where data integration is key to achieving sustainability. The study’s development of an explainable machine-learning framework, utilizing CatBoost for its superior performance, is a significant advancement, allowing for a transparent understanding of the drivers behind flood susceptibility. Furthermore, the incorporation of SHapley Additive explanations (SHAP) to pinpoint key contributing factors like extreme rainfall and wetland characteristics provides actionable insights for targeted mitigation strategies. The application of these techniques moves us closer to a predictive capability that informs proactive adaptation measures.

The finding of a “susceptibility–exposure mismatch” is particularly noteworthy. It highlights that high-risk flood zones don’t necessarily equate to the highest concentration of vulnerable infrastructure. The identification of refined priority zones—covering a relatively small area but containing a disproportionately large number of people, schools, hospitals, and vital transport links—underscores the importance of this service-oriented approach. This aligns with the concept of optimized resource allocation, a theme also explored in our coverage of how AI is enhancing maritime capabilities, such as the U.S Navy Retrains Deep-Sea AI Sonar, where improved monitoring and analysis are used to enhance operational efficiency. The research methodology, combining historical flood data, environmental variables, and exposure receptors, provides a robust foundation for replicating this framework in other rapidly urbanizing estuarine environments globally. The use of a 250m grid resolution strikes a balance between data granularity and computational feasibility, making the approach scalable and applicable across diverse geographical contexts. This is reinforced by recent work on integrated transport systems and marine ecology, as discussed in Coupling coordination between integrated transport–shipping system and nearshore marine ecology, highlighting the complex interplay between infrastructure and environmental health.

The shift towards service-oriented spatial prioritization represents a crucial evolution in coastal flood adaptation planning. Moving beyond simply identifying flood-prone areas to understanding the impact on critical services allows for more targeted and effective interventions. This includes strategic investments in infrastructure resilience, improved early warning systems tailored to specific service disruptions, and the development of evacuation plans that prioritize vulnerable populations and essential facilities. The empirical validation of the framework, with an AUC of 0.870 and a strong F1-score of 0.785, provides confidence in its predictive capabilities and underscores its potential for practical application. The identification of key drivers, such as extreme rainfall and coastal meteorological forcing, further refines our understanding of the underlying processes contributing to coastal flooding, enabling more informed risk management strategies. The longitudinal nature of the data used contributes significantly to the robustness and generalizability of the findings.

Looking ahead, the integration of real-time data streams—including weather forecasts, hydrological monitoring, and traffic information—into this framework promises to enhance its predictive power and operational utility. The development of dynamic, adaptive flood maps that reflect changing conditions could revolutionize coastal resilience planning. Furthermore, the potential for incorporating socioeconomic vulnerability indicators into the exposure analysis could provide a more nuanced understanding of the human impact of coastal flooding. A critical question remains: how can we effectively translate these data-driven insights into actionable policies and investments that ensure the long-term sustainability and resilience of coastal communities facing increasingly frequent and severe flooding events?