Nguyen Huu Duy, Nguyen Quoc-Huy, Dang Dinh Kha, Van Chien Pham, Truong Quang Hai, Pham Si Dung, Bui Quang-Thanh, Petrisor Alexandru-Ionut
Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Thanh Xuan District, Hanoi, Viet Nam.
Faculty of Hydrology, Meteorology, and Oceanography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Thanh Xuan District, Hanoi, Viet Nam.
Sci Total Environ. 2024 Apr 15;921:171204. doi: 10.1016/j.scitotenv.2024.171204. Epub 2024 Feb 23.
Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.
气候变化和城市化进程的加快是全球严重洪灾风险增加的两个主要因素。预测和监测土地利用/土地覆盖（LULC）及气候变化对洪水风险的影响，是制定适当策略以减少潜在损失的关键步骤。本研究旨在通过结合机器学习（即XGBoost、CatBoost、LightGBM和ExtraTree模型）与水力模型，开发一种新方法，以预测气候变化和LULC变化对有洪水风险土地的影响。对于2005年、2020年、203五年和2050年，基于RCP 8.5气候变化情景，利用机器学习对不同LULC情景下的洪水易发性进行建模和预测，同时用水力模型对洪水深度和洪水速度进行建模和预测。这两个要素用于构建洪水风险评估，整合了诸如LULC、人口密度、贫困率、女性数量、学校数量和耕地面积等社会经济数据。然后，通过结合洪水危险度、暴露度和脆弱性，利用层次分析法计算洪水风险。结果表明，高洪水风险和极高洪水风险区域迅速增加，高/极高暴露度区域以及高/极高脆弱性区域也是如此。结果还显示，由于气候变化和LULC变化、人口增长、女性数量和学校数量的动态变化，特别是在洪水区域，洪水风险从2005年到2020年迅速增加，并将在2035年和2050年继续增加。研究结果突出了洪水风险与环境和社会经济变化之间的关系，并表明洪水风险管理策略也应纳入未来的分析中。本研究绘制的地图显示了过去和未来的洪水风险，深入了解了洪水区域城市地区的空间分布，可用于推动优先措施的制定，其中防洪减灾最为重要。
