基于集成学习的地表温度驱动机制时空解耦研究

IF 12 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Ai Wang , Daofeng Liu , Zijing Li , Shaoting Liu , Yunfei Nie , Qiang Zhang
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引用次数: 0

摘要

随着城市化进程的加快和气候变化的影响日益明显,了解城市环境中地表温度的非线性响应机制及其时空非平稳性已成为城市气候学研究的重要热点。以合肥市为例,利用2002年、2013年和2022年的多源遥感数据,结合地理时间加权随机森林和SHAP集成框架(GTWRF-SHAP),系统识别城市发展不同阶段地表温度的多维驱动机制。结果表明:合肥市地表温度分布由“单核集中”向“多核分散”演变;高温区域由中心城区向经济技术开发区、高新区等新兴区域扩展,低温区域集中在水库、森林公园等生态节点周围,呈现出“热岛膨胀、冷源保留”的空间演化特征。随着城市化进程的不断推进,地表温度的主要驱动因素已经明显从以自然因素为主转变为自然、建筑环境和社会经济因素的协同作用。尽管如此,自然因素在局部生态节点的降温效应中仍然发挥着至关重要的作用。研究进一步揭示了关键驱动因素的非线性效应和时空异质性,特别是植被覆盖、建筑高度和容积率等因素的阈值效应。空间上,自然因子的降温效应向城市外围收缩,建筑环境的升温效应向核心区集中,与城市的功能分区和发展阶段紧密契合。该研究增强了对城市化背景下地表温度驱动机制的认识,为气候适应性城市规划提供了重要的科学依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatiotemporal decoupling of land surface temperature driving mechanisms using ensemble learning
As urbanization accelerates and the impacts of climate change become increasingly evident, understanding the nonlinear response mechanisms of land surface temperature (LST) in urban environments, along with its spatiotemporal non-stationarity, has become a significant focus in urban climatology. Using Hefei City as a case study, this research integrates multisource remote sensing data from 2002, 2013, and 2022 with a Geographically and Temporally Weighted Random Forest and SHAP ensemble framework (GTWRF-SHAP) to systematically identify the multidimensional driving mechanisms of LST across different stages of urban development. The results show that the LST distribution in Hefei has evolved from a “single-core concentration” to a “multi-core dispersion” pattern. High-temperature areas have expanded from the central urban area to emerging districts, such as the Economic and Technological Development Zone and the High-Tech Zone, while low-temperature areas remain concentrated around ecological nodes like reservoirs and forest parks, exhibiting a spatial evolution characterized by “heat island expansion and cold source retention.” With ongoing urbanization, the primary drivers of LST have significantly shifted from being predominantly natural factors to a synergy of natural, built environment, and socio-economic factors. Nonetheless, natural factors continue to play a crucial role in the cooling effects at local ecological nodes. The study further reveals significant nonlinear effects and spatiotemporal heterogeneity in key driving factors, particularly the threshold effects of factors such as vegetation cover, building height, and floor area ratio. Spatially, the cooling effect of natural factors has contracted toward the urban periphery, while the warming effect of the built environment has concentrated in the core areas, aligning closely with the city’s functional zoning and development stages. This research enhances the understanding of the driving mechanisms of LST under urbanization and provides essential scientific evidence for climate-adaptive urban planning.
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来源期刊
Sustainable Cities and Society
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;
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