通过全连接神经网络模拟和预测多种情景下的白天地表城市热岛强度

IF 10.5 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Sustainable Cities and Society Pub Date : 2024-10-23 DOI:10.1016/j.scs.2024.105922
Jiongye Li , Yingwei Yan , Rudi Stouffs
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引用次数: 0

摘要

城市化、土地利用和土地覆盖(LULC)变化以及人口增长导致城市地表热岛(SUHI)加剧,给城市地区带来了巨大的环境和公共健康风险。模拟和预测 SUHI,特别是通过识别未来的 SUHI 高强度(SUHII)区,已被视为减轻这些影响的关键步骤。本研究采用了一个全卷积神经网络(FCNN)模型,该模型在四个研究地点的数据基础上进行了训练,利用之前研究中确定的 15 个关键独立变量,模拟了新加坡六个验证地点当前的日间 SUHII。该模型的验证准确率很高,达到 87.45%。根据预测的人口增长和土地利用、土地利用的变化,三种预测方案预测所有验证地点的高 SUHII 都会下降,降幅从 98.3% 到 9% 不等。这一下降归因于 2019 年总体规划中提出的 LULC 改善措施。对预测的 SUHII 地图进行的空间分析表明,在各种方案中,大多数 SUHII 偏高的地点与当前情况保持一致。这项研究还表明,该模型可以成为城市规划者的宝贵工具,使他们能够评估新的城市发展计划是否能有效地将高 SUHII 降低到所需的阈值,从而减轻城市环境中的 SUHII。
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Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network
The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.
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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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