{"title":"利用地理可解释人工智能研究城市洪水因素中非线性空间异质性的影响","authors":"Entong Ke , Juchao Zhao , Yaolong Zhao","doi":"10.1016/j.jhydrol.2024.132398","DOIUrl":null,"url":null,"abstract":"<div><div>Urban pluvial flooding is one of the most significant environmental challenges impacting human society. Understanding the mechanisms through which geographical elements affect flooding is essential for developing effective flood mitigation strategies. However, due to limitations in current research methods, the nonlinear spatial heterogeneity of urban flooding factors remains underexplored. This study aims to design a novel framework based on geographic explainable artificial intelligence (GeoXAI) to investigate the nonlinear spatial heterogeneity of urban flooding factors in a case study of Guangzhou, China. In the attribution analysis of urban flooding susceptibility (UFS), a spatial statistical method and a conventional explainable artificial intelligence method were used for comparative evaluation with the GeoXAI method. The results reveal that: (a) flooding factors exert varying influences across different regions, although they generally increase UFS in the central-southern, western, and southeastern sectors of Guangzhou; (b) kernel normalized difference vegetation index and impervious surface density are dominant factors in urban flooding, with optimal thresholds for effectively mitigating flooding at above 0.25 and below 0.2, respectively; (c) GeoXAI demonstrates superior performance over traditional methods, offering enhanced model accuracy, more reliable interpretability, and better consideration of geospatial variables and spatial effects. These findings provide significant guidance for flood management in Guangzhou and underscore the broader potential of GeoXAI for disaster management in various regions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132398"},"PeriodicalIF":5.9000,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the influence of nonlinear spatial heterogeneity in urban flooding factors using geographic explainable artificial intelligence\",\"authors\":\"Entong Ke , Juchao Zhao , Yaolong Zhao\",\"doi\":\"10.1016/j.jhydrol.2024.132398\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Urban pluvial flooding is one of the most significant environmental challenges impacting human society. Understanding the mechanisms through which geographical elements affect flooding is essential for developing effective flood mitigation strategies. However, due to limitations in current research methods, the nonlinear spatial heterogeneity of urban flooding factors remains underexplored. This study aims to design a novel framework based on geographic explainable artificial intelligence (GeoXAI) to investigate the nonlinear spatial heterogeneity of urban flooding factors in a case study of Guangzhou, China. In the attribution analysis of urban flooding susceptibility (UFS), a spatial statistical method and a conventional explainable artificial intelligence method were used for comparative evaluation with the GeoXAI method. The results reveal that: (a) flooding factors exert varying influences across different regions, although they generally increase UFS in the central-southern, western, and southeastern sectors of Guangzhou; (b) kernel normalized difference vegetation index and impervious surface density are dominant factors in urban flooding, with optimal thresholds for effectively mitigating flooding at above 0.25 and below 0.2, respectively; (c) GeoXAI demonstrates superior performance over traditional methods, offering enhanced model accuracy, more reliable interpretability, and better consideration of geospatial variables and spatial effects. These findings provide significant guidance for flood management in Guangzhou and underscore the broader potential of GeoXAI for disaster management in various regions.</div></div>\",\"PeriodicalId\":362,\"journal\":{\"name\":\"Journal of Hydrology\",\"volume\":\"648 \",\"pages\":\"Article 132398\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022169424017943\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022169424017943","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Investigating the influence of nonlinear spatial heterogeneity in urban flooding factors using geographic explainable artificial intelligence
Urban pluvial flooding is one of the most significant environmental challenges impacting human society. Understanding the mechanisms through which geographical elements affect flooding is essential for developing effective flood mitigation strategies. However, due to limitations in current research methods, the nonlinear spatial heterogeneity of urban flooding factors remains underexplored. This study aims to design a novel framework based on geographic explainable artificial intelligence (GeoXAI) to investigate the nonlinear spatial heterogeneity of urban flooding factors in a case study of Guangzhou, China. In the attribution analysis of urban flooding susceptibility (UFS), a spatial statistical method and a conventional explainable artificial intelligence method were used for comparative evaluation with the GeoXAI method. The results reveal that: (a) flooding factors exert varying influences across different regions, although they generally increase UFS in the central-southern, western, and southeastern sectors of Guangzhou; (b) kernel normalized difference vegetation index and impervious surface density are dominant factors in urban flooding, with optimal thresholds for effectively mitigating flooding at above 0.25 and below 0.2, respectively; (c) GeoXAI demonstrates superior performance over traditional methods, offering enhanced model accuracy, more reliable interpretability, and better consideration of geospatial variables and spatial effects. These findings provide significant guidance for flood management in Guangzhou and underscore the broader potential of GeoXAI for disaster management in various regions.
期刊介绍:
The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.