Urban expansion trends and their relationship with flood susceptibility during the period 2014–2024 in Hanoi City

Over the past few decades, urban expansion has accelerated worldwide. This process can increase future flood risks due to local changes in hydrological conditions and the increased exposure and vulnerability of communities in flood-prone areas. Therefore, assessing the impact of urban expansion on flood susceptibility is an important task that can support local authorities in urban planning and in mitigating flood impacts. The objective of this study was to assess the impact of urban expansion on flood susceptibility in Hanoi using machine learning models: Deep Neural Networks (DNN), Adaptive Boosting (ADB), Extreme Gradient Boosting (XGB), and Random Forest (RF). A total of 1058 flood points and 14 conditioning factors corresponding to 2014 and 2024 were used as input to the models. Statistical indices, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Area Under the Curve (AUC), and Coefficient of Determination (R2) were used to evaluate the performance of the proposed model. The results showed that the DNN model achieved the highest performance in assessing the impact of urban expansion on flood susceptibility (AUC=0.92), followed by XGB (0.91), ADB (0.86), and RF (0.82). During 2014–2024, urban expansion combined with the impacts of climate change has significantly increased the areas susceptible to flooding. In Hanoi, areas in the "high" and "very high" flood-susceptibility categories have been expanding continuously, accounting for about 25% of the total study area. In contrast, the "medium" group has a slight decreasing trend, while the "low" and "very low" areas have narrowed. This shows that urban expansion is increasing the area prone to flooding. The results of this study provide a solid scientific basis, supporting planners and policymakers in identifying limitations in current flood risk adaptation measures and in developing more appropriate spatial and temporal strategies to minimize flood impacts.