Spatio-temporal modeling of asthma-prone areas: Exploring the influence of urban climate factors with explainable artificial intelligence (XAI)

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-10-09 DOI:10.1016/j.scs.2024.105889

Seyed Vahid Razavi-Termeh , Abolghasem Sadeghi-Niaraki , Farman Ali , Rizwan Ali Naqvi , Soo-Mi Choi

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引用次数: 0

Abstract

Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3 % in spring, 97.5 % in summer, 97.8 % in autumn, and 98.4 % in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM_2.5) in spring, carbon monoxide (CO) in summer, ozone (O₃) in autumn, and PM₁₀ in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization.

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哮喘易发区的时空建模：利用可解释人工智能（XAI）探索城市气候因素的影响

人们日益认识到，城市化对气候的影响是一项重大的公共卫生挑战，尤其是对哮喘等呼吸系统疾病而言。尽管在了解哮喘方面取得了进展，但在城市环境因素与哮喘易发地区之间的相互作用方面仍存在重大差距。本研究通过应用创新的时空建模技术和可解释人工智能（XAI）来弥补这一不足。利用伊朗德黑兰 872 名哮喘患者的数据和 19 个影响哮喘恶化的因素（包括气候和空气污染），采用蝙蝠算法（BA）优化的 XGBoost（eXtreme Gradient Boosting）算法进行了时空建模。使用接收器操作特征曲线（ROC）对哮喘易发区地图进行评估后发现，春季的准确率为 97.3%，夏季为 97.5%，秋季为 97.8%，冬季为 98.4%。利用 SHAP（Shapley Additive exPlanations）方法对 XGBoost 模型进行的可解释性分析表明，春季和秋季的降雨量以及夏季和冬季的气温对哮喘的影响最大。在空气污染因素中，春季的颗粒物（PM2.5）、夏季的一氧化碳（CO）、秋季的臭氧（O3）和冬季的可吸入颗粒物（PM10）对哮喘的影响最大。这项研究加深了人们对城市环境中哮喘动态的了解，为城市规划战略提供了有针对性的干预措施，以减轻城市化对健康造成的不利影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;