Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models to explore future urban flood risk, aiming to reduce it under different vulnerability and exposure scenarios. SPOT-3 and Sentinel-2 images were processed and classified to create land cover maps for 1995 and 2019, and these were used to predict the 2040 land cover using the Land Change Modeler Module of Terrset. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. We have compared flood risk in 1995, 2019, and 2040. Although flood risk increases with urbanization, population density, and the number of hospitals in the flood plain, especially in the coastal region, the area exposed to high and very high risks decreases due to a reduction in poverty rate. This study can provide a theoretical framework supporting climate change related to risk assessment in other metropolitan regions. Methodologically, it underlines the importance of using satellite imagery and the continuity of data in the planning-related decision-making process.

This study aims to develop a comprehensive approach including an analysis of the relationships between flood susceptibility and land-use change, based on the relevance vector machine (RVM) and coyote optimization algorithm (COA) models, applied to Gianh River watershed, Quang Binh province, Central Vietnam. Standard statistical indices, e.g. area under the curve (AUC), were used to assess the model performance. Comparative analyses emphasize that the COA successfully improves the performance of the RVM model (AUC = 0.99) and is also better than the reference models such as support vector machine (AUC = 0.98), gradient boosting machine (AUC = 0.97), random forest (AUC = 0.99), extra trees regressor (AUC = 0.98), and AdaBoost (AUC = 0.96). The improved model, when used in conjunction with land use maps, is able to show that urbanization has increased in flood-susceptible areas. The results highlight that urbanization has increased in the low and very low flood susceptibility areas by 110% between 2005 and 2020, while in the high and very high areas it has increased by 30 to 40%, despite urban and demographic growth.

Floods are the most dangerous natural disasters globally, occurring on a large scale, and cause significant economic and environmental damage. Therefore, determining flood susceptibility is essential to reducing the flood effects on human lives and materials. The main objective of this research is to develop a novel hybrid algorithm, through combining deep neural network and Manta ray foraging optimization (DNN-MRFO), to generate flood susceptibility map for Quang Ngai province, Vietnam. A geospatial distribution analytical approach was used to generate input data, including 2176 flood locations points and 13 influencing factors. A comparative analysis of the proposed model with five models namely DNN – particle swarm optimization (DNN-PSO), DNN – grey wolf optimization (DNN-GWO), DNN – social spider optimization (DNN-SSO), support vector machine (SVM), gradient boosting regression (GBR) was carried out using different evaluation indices. The result shows that combining DNN and MRFO improved flood susceptibility classification precision with an area under the curve (AUC) of 0.98. The findings of this study are significant for supporting policymakers in understanding and identifying issues, which support improve their adaptation strategies.