Assessing future flood risk using remote sensing and explainable machine learning: A case study in the Beijing-Tianjin-Hebei region

IF 4.5 Q2 ENVIRONMENTAL SCIENCES
Siying Li , Yi Tang , Yuting Zhao , Xiaojun Ning , Yifan Zhang , Siran Lv , Chenshu Liu
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引用次数: 0

Abstract

Flood disasters pose increasingly severe threats to densely populated and economically critical regions under changing climate conditions. In this study, we conducted a comprehensive flood risk assessment of the Beijing-Tianjin-Hebei (BTH) region, integrating multi-source remote sensing data and explainable machine learning methods. First, flood inundation areas during the 2023 extreme rainfall event were identified using Sentinel-1 SAR imagery. Based on the assessment framework of hazard, exposure, and vulnerability, key factors influencing flood risk were quantified using a XGBboost model and SHAP (Shapley Additive Explanations) analysis. The results revealed that terrain ruggedness, elevation, precipitation, dependency ratio, and GDP (Gross Domestic Product) were the primary drivers of flood risk distribution. Subsequently, future flood risk patterns for 2030 were projected under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585), combining projected environmental and socio-economic variables with the established model. The findings indicate a clear trend of flood risk intensification under higher emission scenarios, with high-risk areas expanding significantly under SSP370 and SSP585. These results emphasize the urgent need for differentiated flood management strategies, combining climate mitigation, resilient urban planning, and adaptive infrastructure development to effectively reduce future flood risks. It provides a scientific basis for climate-resilient disaster risk governance in rapidly urbanizing regions.
基于遥感和可解释机器学习的未来洪水风险评估——以京津冀地区为例
在不断变化的气候条件下,洪水灾害对人口密集和经济关键地区的威胁日益严重。在本研究中,我们结合多源遥感数据和可解释的机器学习方法,对京津冀(BTH)地区进行了洪水风险综合评估。首先,利用Sentinel-1 SAR图像识别2023年极端降雨事件的洪水淹没区域。基于危害、暴露和脆弱性评估框架,采用XGBboost模型和SHAP (Shapley Additive explanation)分析对影响洪水风险的关键因素进行量化。结果表明,地形起伏度、海拔、降水、抚养比和国内生产总值(GDP)是影响洪水风险分布的主要因素。随后,将预测的环境和社会经济变量与建立的模型相结合,在4条共享社会经济路径(SSP126、SSP245、SSP370和SSP585)下预测了2030年的未来洪水风险模式。结果表明,高排放情景下洪涝风险加剧趋势明显,其中SSP370和SSP585情景下洪涝风险区显著扩大。这些结果强调,迫切需要采取差异化的洪水管理策略,将气候缓解、弹性城市规划和适应性基础设施发展相结合,以有效降低未来的洪水风险。它为快速城市化地区的气候适应型灾害风险治理提供了科学依据。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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