Smart City Gnosys
Smart city article details
| Title | A Machine Learning Approach For Mapping Surface Urban Heat Island Using Environmental And Socioeconomic Variables: A Case Study In A Medium-Sized Brazilian City |
|---|---|
| ID_Doc | 2457 |
| Authors | Furuya M.T.G.; Furuya D.E.G.; de Oliveira L.Y.D.; da Silva P.A.; Cicerelli R.E.; Gonçalves W.N.; Junior J.M.; Osco L.P.; Ramos A.P.M. |
| Year | 2023 |
| Published | Environmental Earth Sciences, 82, 13 |
| DOI | http://dx.doi.org/10.1007/s12665-023-11017-8 |
| Abstract | Smart cities must deal with climate change and find solutions to mitigate phenomena such as urban heat islands (UHI). The land surface temperature (LST) extracted from thermal images is a primary source of information to study UHI, characterizing the surface urban heat islands (SUHI). In addition to LST, environmental and socioeconomic variables have been adopted to explain the SUHI phenomenon. Although machine learning algorithms have potential in several areas, their application in the study of the contribution of these variables in the prediction of LST to characterize SUHI is still unknown. Therefore, the work proposes a machine learning approach to fill this gap. The LST was extracted from 15 Landsat 8 images from 2019 to 2021. Data on socioeconomic variables were obtained from the official demographic census, and environmental variables were extracted from Sentinel-2 and Planet images. Six algorithms were tested to assess the ability to estimate the LST based on the above-mentioned variables. The results showed that the Decision Tree algorithm had the best performance (r = 0.96, MAE = 1.49 °C and RMSE = 1.88 °C), followed by Random Forest. In addition, the inclusion of all seasons of the year and socioeconomic variables was shown to be relevant to the results. The main contribution of this work is to verify if the algorithms can optimize the SUHI characterization process, analyzing the influence exerted by the studied variables. In the social sphere, the information produced can help urban planning in the construction of smart cities. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| Author Keywords | Decision tree; Land surface temperature; Machine learning; Remote sensing; Surface urban heat island |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
36847  | 0.898 | Le M.T.; Bakaeva N.; Danilina N.; Hoang T.V.A. | Method Of Identifying Urban Heat Islands By Remote Sensing Based On Big Data | E3S Web of Conferences, 403 (2023) |
| 58170 | 0.892 | Rickens B.; Tonekaboni N.H. | Towards High Resolution Urban Heat Analysis: Incorporating Thermal Drones To Enhance Satellite Based Urban Heatmaps | ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023 (2023) |
| 60012 | 0.886 | Kisters P.; Ngu V.; Edinger J. | Urban Heat Island Detection Utilizing Citizen Science | Communications in Computer and Information Science, 1617 CCIS (2022) |
| 9136 | 0.886 | Sahoo S.; Majumder A.; Swain S.; Gareema; Pateriya B.; Al-Ansari N. | Analysis Of Decadal Land Use Changes And Its Impacts On Urban Heat Island (Uhi) Using Remote Sensing-Based Approach: A Smart City Perspective | Sustainability (Switzerland), 14, 19 (2022) |
| 39272 | 0.884 | Dutta K.; Basu D.; Agrawal S. | Nocturnal And Diurnal Trends Of Surface Urban Heat Island Intensity: A Seasonal Variability Analysis For Smart Urban Planning | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 5/W2 (2019) |
| 10704 | 0.88 | Ghosh S.; Kumar D.; Kumari R. | Assessing Spatiotemporal Dynamics Of Land Surface Temperature And Satellite-Derived Indices For New Town Development And Suburbanization Planning | Urban Governance, 2, 1 (2022) |
| 36999 | 0.876 | Iizuka K.; Akiyama Y.; Takase M.; Fukuba T.; Yachida O. | Microscale Temperature-Humidity Index (Thi) Distribution Estimated At The City Scale: A Case Study In Maebashi City, Gunma Prefecture, Japan | Remote Sensing, 16, 17 (2024) |
| 21197 | 0.874 | Chauhan S.; Jethoo A.S.; Mishra A.; Varshney V. | Duo Satellite-Based Remotely Sensed Land Surface Temperature Prediction By Various Methods Of Machine Learning | International Journal of Data Science and Analytics, 18, 4 (2024) |
| 2725 | 0.867 | Nandini G.; Vinoj V.; Sethi S.S.; Nayak H.P.; Landu K.; Swain D.; Mohanty U.C. | A Modelling Study On Quantifying The Impact Of Urbanization And Regional Effects On The Wintertime Surface Temperature Over A Rapidly-Growing Tropical City | Computational Urban Science, 2, 1 (2022) |
| 47532 | 0.862 | Abunnasr Y.; Mhawej M.; Chrysoulakis N. | Sebu: A Novel Fully Automated Google Earth Engine Surface Energy Balance Model For Urban Areas | Urban Climate, 44 (2022) |