Flood risk assessment of coastal cities based on GCW_ISODATA and explainable artificial intelligence methods

Abstract

Scientific and accurate assessment of risk influencing factors are crucial for flood risk management. This paper aims to propose a new comprehensive framework for flood risk assessment in coastal cities. Firstly, considering the flood characteristics of coastal cities and the impact of floods on urban spatial structure, a flood risk assessment indicator system for coastal cities was established. Secondly, combining game combination weighting and Iterative self-organizing data analysis technique algorithm (GCW_ISODATA) for flood risk assessment. Finally, based on the explainable machine learning techniques, the sensitivity of indicators to flood risk was analyzed. The results indicated that coastal floods are more destructive than rainfall and river floods. Moreover, the indicator weighting and threshold division have a direct impact on the rationality of flood risk, GCW_ISODATA method performs well in Accuracy, F1 score, and AUC, especially with the highest AUC among all methods. Entropy weight method and GCW are significantly superior to Analytic Hierarchy Process (AHP) method, and ISODATA method usually performs better than the K-Means and Natural Break method. Furthermore, the sensitivity of indicators to flood risk reveals that differences in economic, social, and environmental characteristics across regions affect the actual impact of these indicators, leading to the sensitivity of the same indicator to flood risk varies significantly across different regions. It is expected that the framework proposed in this study can be used to explore flood risk impact on other coastal cities.