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Cited by 55Urban Heat Island MitigationSmart Cities and TechnologiesLand Use and Ecosystem Services

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An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data

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Lyu, Fangzheng, Wang, Shaohua, Han, Su Yeon, Catlett, Charlie, Wang, Shaowen·2022·Urban Informatics
DOI 10.1007/s44212-022-00002-4PMC PMC9458483
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Abstract

Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM 2.5 concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.

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Crossref · 10.1007/s44212-022-00002-4

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Fig. 1 Weather Underground, among the densest weather station networks, is absent in vast regions on Chicago’s South and West sides. (Source: www.wunderground.com)
Fig. 1 Weather Underground, among the densest weather station networks, is absent in vast regions on Chicago’s South and West sides. (Source: www.wunderground.com)CC BY · PubMed Central
Fig. 2 Network of 130 Array of Things (AoT) intelligent sensor nodes in Chicago
Fig. 2 Network of 130 Array of Things (AoT) intelligent sensor nodes in ChicagoCC BY · PubMed Central
Fig. 3 cyberGIS framework
Fig. 3 cyberGIS frameworkCC BY · PubMed Central
Fig. 4 The first three layers of the first decision tree and feature importance in the random forest approach (2018, 2019 and 2020)
Fig. 4 The first three layers of the first decision tree and feature importance in the random forest approach (2018, 2019 and 2020)CC BY · PubMed Central
Fig. 5 Temporal distribution of MAE for random forest regression
Fig. 5 Temporal distribution of MAE for random forest regressionCC BY · PubMed Central
Fig. 6 From left to right for 2018.8.27: observed UHI pattern, UHI pattern predicted with RFR, and UHI pattern predicted with ANN
Fig. 6 From left to right for 2018.8.27: observed UHI pattern, UHI pattern predicted with RFR, and UHI pattern predicted with ANNCC BY · PubMed Central
Fig. 7 From left to right on 2019.7.20: observed UHI pattern, UHI pattern predicted with RFR, and UHI pattern predicted with ANN
Fig. 7 From left to right on 2019.7.20: observed UHI pattern, UHI pattern predicted with RFR, and UHI pattern predicted with ANNCC BY · PubMed Central
Fig. 8 Predicted temperature with the RFR model against temperature detected by AoT sensors on the hottest day in 2018 and 2019 respectively
Fig. 8 Predicted temperature with the RFR model against temperature detected by AoT sensors on the hottest day in 2018 and 2019 respectivelyCC BY · PubMed Central
Fig. 9 Prediction of UHI clusters in Chicago on 2018.8.27
Fig. 9 Prediction of UHI clusters in Chicago on 2018.8.27CC BY · PubMed Central

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Metadata

Authors
Lyu, F., Wang, S., Han, S. Y., Catlett, C., & Wang, S.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • Argonne National Laboratory (US)
Corresponding author
  • Wang, Shaohua
  • Wang, Shaowen
ORCID
Venue
Urban Informatics
Publisher
Springer Science and Business Media LLC
Published
2022-09-09
Volume
1
Issue
1
Pages
6-6
ISSN
2731-6963 (ISSN-L)
DOI
10.1007/s44212-022-00002-4
PMC
PMC9458483
OpenAlex
W4297848564
Cited by
55
References
2
Citations by year
2026: 11 · 2025: 20 · 2024: 13 · 2023: 9 · 2022: 2
FWCI
3.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Urban Informatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Directorate for Geosciences (1833225)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Social Sciences › Urban Studies › Smart Cities and Technologies
  • Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
Keywords
urban heat island, CyberGIS, urban sensor networks, Landsat 8, random forest regression, high-frequency sensor data, PM2.5, humidity, Chicago

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