{"title":"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","authors":"Jiongye Li , Yingwei Yan , Rudi Stouffs","doi":"10.1016/j.scs.2024.105922","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105922"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network\",\"authors\":\"Jiongye Li , Yingwei Yan , Rudi Stouffs\",\"doi\":\"10.1016/j.scs.2024.105922\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"116 \",\"pages\":\"Article 105922\"},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670724007467\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007467","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
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.
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;