Xin Yang , Yulu Hao , Hanyue Ding , Changshui Yu , Jia Liu , Linyao Li , Junmin Chen
{"title":"基于空间分布特征的基于XGBoost和SHAP的可解释人工智能(XAI)框架评估城市火灾风险","authors":"Xin Yang , Yulu Hao , Hanyue Ding , Changshui Yu , Jia Liu , Linyao Li , Junmin Chen","doi":"10.1016/j.ijdrr.2025.105798","DOIUrl":null,"url":null,"abstract":"<div><div>Urban fire risks intensify owing to rapid urbanization, increasing population density, and concentrated infrastructure. Traditional fire risk assessment models, such as Multi-scale Geographically Weighted Regression (MGWR), often overlook spatial heterogeneity and lack interpretability. This study applied an eXplainable Artificial Intelligence (XAI) framework integrating eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for predictive modeling and for feature attribution, respectively, to assess the urban fire risk in Qingyang District in Chengdu, China. A key challenge was to address the spatial complexity of fire risks and ensure model interpretability for decision-makers. To overcome this challenge, we integrated spatial autocorrelation metrics and kernel density analysis to analyze the spatial patterns of key features. The framework outperformed MGWR in terms of prediction accuracy, with R<sup>2</sup> values of 0.998 for fire occurrence and 0.935 for fire loss, compared to the 0.995 and 0.856 obtained for MGWR, respectively. The mean squared error was 4.447 for fire occurrence and 0.00027 for fire loss, while 10.297 and 0.00055 were obtained for MGWR, respectively. Notably, the SHAP values proved to be effective in explaining the spatial heterogeneity of urban fire risk features. The main contributions of this study are as follows: (1) the integration of XGBoost and SHAP for interpretable urban fire risk prediction, (2) the application of spatial analysis to enhance model transparency, and (3) the development of a framework that provides actionable, spatially explicit insights for urban safety planning. This study provides a data-driven tool for policymakers that offers quantitative guidance for urban fire risk mitigation.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"129 ","pages":"Article 105798"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Explainable Artificial Intelligence (XAI) framework using XGBoost and SHAP for assessing urban fire risk based on spatial distribution features\",\"authors\":\"Xin Yang , Yulu Hao , Hanyue Ding , Changshui Yu , Jia Liu , Linyao Li , Junmin Chen\",\"doi\":\"10.1016/j.ijdrr.2025.105798\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Urban fire risks intensify owing to rapid urbanization, increasing population density, and concentrated infrastructure. Traditional fire risk assessment models, such as Multi-scale Geographically Weighted Regression (MGWR), often overlook spatial heterogeneity and lack interpretability. This study applied an eXplainable Artificial Intelligence (XAI) framework integrating eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for predictive modeling and for feature attribution, respectively, to assess the urban fire risk in Qingyang District in Chengdu, China. A key challenge was to address the spatial complexity of fire risks and ensure model interpretability for decision-makers. To overcome this challenge, we integrated spatial autocorrelation metrics and kernel density analysis to analyze the spatial patterns of key features. The framework outperformed MGWR in terms of prediction accuracy, with R<sup>2</sup> values of 0.998 for fire occurrence and 0.935 for fire loss, compared to the 0.995 and 0.856 obtained for MGWR, respectively. The mean squared error was 4.447 for fire occurrence and 0.00027 for fire loss, while 10.297 and 0.00055 were obtained for MGWR, respectively. Notably, the SHAP values proved to be effective in explaining the spatial heterogeneity of urban fire risk features. The main contributions of this study are as follows: (1) the integration of XGBoost and SHAP for interpretable urban fire risk prediction, (2) the application of spatial analysis to enhance model transparency, and (3) the development of a framework that provides actionable, spatially explicit insights for urban safety planning. This study provides a data-driven tool for policymakers that offers quantitative guidance for urban fire risk mitigation.</div></div>\",\"PeriodicalId\":13915,\"journal\":{\"name\":\"International journal of disaster risk reduction\",\"volume\":\"129 \",\"pages\":\"Article 105798\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of disaster risk reduction\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212420925006223\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212420925006223","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Explainable Artificial Intelligence (XAI) framework using XGBoost and SHAP for assessing urban fire risk based on spatial distribution features
Urban fire risks intensify owing to rapid urbanization, increasing population density, and concentrated infrastructure. Traditional fire risk assessment models, such as Multi-scale Geographically Weighted Regression (MGWR), often overlook spatial heterogeneity and lack interpretability. This study applied an eXplainable Artificial Intelligence (XAI) framework integrating eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for predictive modeling and for feature attribution, respectively, to assess the urban fire risk in Qingyang District in Chengdu, China. A key challenge was to address the spatial complexity of fire risks and ensure model interpretability for decision-makers. To overcome this challenge, we integrated spatial autocorrelation metrics and kernel density analysis to analyze the spatial patterns of key features. The framework outperformed MGWR in terms of prediction accuracy, with R2 values of 0.998 for fire occurrence and 0.935 for fire loss, compared to the 0.995 and 0.856 obtained for MGWR, respectively. The mean squared error was 4.447 for fire occurrence and 0.00027 for fire loss, while 10.297 and 0.00055 were obtained for MGWR, respectively. Notably, the SHAP values proved to be effective in explaining the spatial heterogeneity of urban fire risk features. The main contributions of this study are as follows: (1) the integration of XGBoost and SHAP for interpretable urban fire risk prediction, (2) the application of spatial analysis to enhance model transparency, and (3) the development of a framework that provides actionable, spatially explicit insights for urban safety planning. This study provides a data-driven tool for policymakers that offers quantitative guidance for urban fire risk mitigation.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.