A novel combination of deep neural network and Manta ray foraging optimization for flood susceptibility mapping in Quang Ngai province, Vietnam

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

Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam

Understanding the negative effects of climate change and changes to land use/land cover on natural hazards is an important feature of sustainable development worldwide, as these phenomena are inextricably linked with natural hazards such as landslides. The contribution of this study is an attempt to develop a state-of-the-art method to assess the effects of climate change and changes in land use/land cover on landslide susceptibility in the Tra Khuc river basin in Vietnam. The method is based on machine learning and remote sensing algorithms, namely radial basis function neural networks–search and rescue optimization (RBFNN–SARO), radial basis function neural network–queuing search algorithm (RBFNN–QSA), radial basis function neural network–life choice-based optimizer (RBFNN–LCBO), radial basis function neural network–dragonfly optimization (RBFNN–DO). All proposed models performed well, with AUC value of >0.9. The RBFNN–QSA model performed best, with an AUC value of 0.98, followed by RBFNN–SARO (AUC = 0.97), RBFNN–LCBO (AUC = 0.95), RBFNN–DO (AUC = 0.93), and support vector machine (SVM; AUC = 0.92). The results show that both climate and land use/land cover change greatly in the future: Precipitation increases 18% by 2030 and 25.1% by 2050; the total production forest, protected forest and built-up area change considerably between 2010 and 2050. These changes influence landslide susceptibility: The area of high and very high landslide susceptibility decrease by approximately 100 and 300 km2 respectively in the study area from 2010 to 2050. The findings of this study can support decision-makers in formulating appropriate strategies to reduce damage from landslides, such as limiting construction in areas where future landslides are predicted. Although this study applies to a particular region of Vietnam, the findings can be applied in other mountainous regions around the world

Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam

International audienceFlood 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