Study on the influence mechanism of urban environmental factors on thermal environment based on machine learning method | Environment, Development and Sustainability
Acero, J. A., Koh, E. J., Ruefenacht, L. A., & Norford, L. K. (2021). Modelling the influence of high-rise urban geometry on outdoor thermal comfort in Singapore. Urban Climate, 36, Article 100775.
Bai, Y., Wang, W., Liu, M., Xiong, X., & Li, S. (2024). Impact of urban greenspace on the urban thermal environment: A case study of Shenzhen, China. Sustainable Cities and Society, 112, Article 105591. https://doi.org/10.1016/j.scs.2024.105591
Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. Paper presented at the Proceedings of the European conference on computer vision (ECCV).
Chiang, Y. C., Liu, H. H., Li, D. Y., & Ho, L. C. (2023). Quantification through deep learning of sky view factor and greenery on urban streets during hot and cool seasons. Landscape and Urban Planning. https://doi.org/10.1016/j.landurbplan.2022.104679
Dai, Z. X., Guldmann, J. M., & Hu, Y. F. (2019). Thermal impacts of greenery, water, and impervious structures in Beijing’s Olympic area: A spatial regression approach. Ecological Indicators, 97, 77–88. https://doi.org/10.1016/j.ecolind.2018.09.041
Deng, H. J., Zhang, S. R., Chen, M. H., Feng, J. L., & Liu, K. (2024). Sensitivity of Local Climate Zones and Urban Functional Zones to Multi-Scenario Surface Urban Heat Islands. Remote Sensing. https://doi.org/10.3390/rs16163048
Fan, Q., Song, X. N., Shi, Y., & Gao, R. (2021). Influencing factors of spatial heterogeneity of Land Surface Temperature in Nanjing, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8341–8349. https://doi.org/10.1109/jstars.2021.3105582
Guo, G. H., Wu, Z. F., & Chen, Y. B. (2019). Complex mechanisms linking land surface temperature to greenspace spatial patterns: Evidence from four southeastern Chinese cities. Science of the Total Environment, 674, 77–87. https://doi.org/10.1016/j.scitotenv.2019.03.402
Han, L., Zhao, J. Y., Gao, Y. J., & Gu, Z. L. (2022). Prediction and evaluation of spatial distributions of ozone and urban heat island using a machine learning modified land use regression method. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2021.103643
Han, S., Hou, H., Estoque, R. C., Zheng, Y., Shen, C., Murayama, Y., Pan, J., Wang, B., & Hu, T. (2023). Seasonal effects of urban morphology on land surface temperature in a three-dimensional perspective: A case study in Hangzhou, China. Building and environment. https://doi.org/10.1016/j.buildenv.2022.109913
Hollmann, N., Müller, S., Purucker, L., Krishnakumar, A., Körfer, M., Hoo, S. B., & Hutter, F. (2025). Accurate predictions on small data with a tabular foundation model. Nature, 637(8045), 319–326.
Jiang, S., Zhan, W., Yang, J., Liu, Z., & Huang, F. (2020). Research progress on spatiotemporal differentiation of urban heat island under the framework of local climate zoning. Journal of Geographical Sciences, 75, 1860–1878.
Kabano, P., Lindley, S., & Harris, A. (2021). Evidence of urban heat island impacts on the vegetation growing season length in a tropical city. Landscape and Urban Planning, 206, Article 103989.
Khan, M. S., Ullah, S., Sun, T., Rehman, A. U. R., & Chen, L. D. (2020). Land-Use/Land-Cover Changes and Its Contribution to Urban Heat Island: A Case Study of Islamabad, Pakistan. Sustainability. https://doi.org/10.3390/su12093861
Kim, Y., & Kim, Y. (2022). Explainable heat-related mortality with random forest and SHapley Additive exPlanations (SHAP) models. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2022.103677
Kolokotsa, D., Lilli, K., Gobakis, K., Mavrigiannaki, A., Haddad, S., Garshasbi, S., Mohajer, H. R. H., Paolini, R., Vasilakopoulou, K., Bartesaghi, C., Prasad, D., & Santamouris, M. (2022). Analyzing the Impact of Urban Planning and Building Typologies in Urban Heat Island Mitigation. Buildings. https://doi.org/10.3390/buildings12050537
Li, J. Y., Sun, R. H., Liu, T., Xie, W., & Chen, L. D. (2021). Prediction models of urban heat island based on landscape patterns and anthropogenic heat dynamics. Landscape Ecology, 36(6), 1801–1815. https://doi.org/10.1007/s10980-021-01246-2
Liang, Z., Wu, S., Wang, Y., Wei, F., Huang, J., Shen, J., & Li, S. (2020). The relationship between urban form and heat island intensity along the urban development gradients. Science of the Total Environment, 708, Article 135011.
Liu, X., Ming, Y. J., Liu, Y., Yue, W. Z., & Han, G. F. (2022). Influences of landform and urban form factors on urban heat island: Comparative case study between Chengdu and Chongqing. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2022.153395
Liu, F., Liu, J., Zhang, Y. Q., Hong, S. P., Fu, W. C., Wang, M. H., & Dong, J. W. (2024). Construction of a cold island network for the urban heat island effect mitigation. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2024.169950
Liu, Y., An, Z. H., & Ming, Y. J. (2024). Simulating influences of land use/land cover composition and configuration on urban heat island using machine learning. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2024.105482
Nikolopoulou, M., & Lykoudis, S. (2006). Thermal comfort in outdoor urban spaces: Analysis across different European countries. Building and Environment, 41(11), 1455–1470.
Peng, J., Xie, P., Liu, Y. X., & Ma, J. (2016). Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sensing of Environment, 173, 145–155. https://doi.org/10.1016/j.rse.2015.11.027
Peng, F., Wong, M. S., Ho, H. C., Nichol, J., & Chan, P. W. (2017). Reconstruction of historical datasets for analyzing spatiotemporal influence of built environment on urban microclimates across a compact city. Building and Environment, 123, 649–660.
Qian, Y., Chakraborty, T., Li, J., Li, D., He, C., Sarangi, C., & Leung, L. R. (2022). Urbanization impact on regional climate and extreme weather: Current understanding, uncertainties, and future research directions. Advances in Atmospheric Sciences, 39(6), 819–860.
Qiao, Z., Tian, G., & Xiao, L. (2013). Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data. ISPRS Journal of Photogrammetry and Remote Sensing, 85, 93–101.
Sekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2020.107230
Song, J., Wang, Z. H., Myint, S. W., & Wang, C. (2017). The hysteresis effect on surface-air temperature relationship and its implications to urban planning: An examination in Phoenix, Arizona, USA. Landscape and Urban Planning, 167, 198–211.
Tang, Y., Zhang, J., Liu, R., & Li, Y. (2022). Exploring the impact of built environment attributes on social followings using social media data and deep learning. ISPRS International Journal of Geo-Information, 11(6), Article 325.
Tatem, A. J. (2017). WorldPop, open data for spatial demography. Scientific Data, 4(1), 1–4.
Wan, Y., Du, H., Yuan, L., Xu, X., Tang, H., & Zhang, J. (2025). Exploring the influence of block environmental characteristics on land surface temperature and its spatial heterogeneity for a high-density city. Sustainable Cities and Society, 118, Article 105973. https://doi.org/10.1016/j.scs.2024.105973
Wang, Y., Li, X., Zhang, C., & He, W. (2022). Influence of spatiotemporal changes of impervious surface on the urban thermal environment: A case of Huai’an central urban area. Sustainable Cities and Society, 79, Article 103710. https://doi.org/10.1016/j.scs.2022.103710
Wang, Y. Y., Liang, Z., Ding, J. Q., Shen, J. S., Wei, F. L., & Li, S. C. (2022b). Prediction of Urban Thermal Environment Based on Multi-Dimensional Nature and Urban Form Factors. Atmosphere. https://doi.org/10.3390/atmos13091493
Wu, Z., & Zhang, Y. (2018). Spatial variation of urban thermal environment and its relation to green space patterns: Implication to sustainable landscape planning. Sustainability, 10(7), Article 2249.
Wu, W. B., Yu, Z. W., Ma, J., & Zhao, B. (2022). Quantifying the influence of 2D and 3D urban morphology on the thermal environment across climatic zones. Landscape and Urban Planning. https://doi.org/10.1016/j.landurbplan.2022.104499
Wu, Y., Li, J., & Yang, J. (2023). Using Improved DeepLabV3 + for Complex Scene Segmentation. Paper presented at the 2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE).
Yang, C., & Zhao, S. (2023). Diverse seasonal hysteresis of surface urban heat islands across Chinese cities: Patterns and drivers. Remote Sensing of Environment, 294, Article 113644.
Yang, J., Wang, Y., Xiao, X., Jin, C., Xia, J., & Li, X. (2019). Spatial differentiation of urban wind and thermal environment in different grid sizes. Urban Climate, 28, Article 100458. https://doi.org/10.1016/j.uclim.2019.100458
Yang, J., Yang, Y., Sun, D., Jin, C., & Xiao, X. (2021). Influence of urban morphological characteristics on thermal environment. Sustainable Cities and Society, 72, Article 103045.
Yao, Y., Yin, H., Xu, C., Chen, D., Shao, L., Guan, Q., & Wang, R. (2022). Assessing myocardial infarction severity from the urban environment perspective in Wuhan, China. Journal of Environmental Management, 317, Article 115438.
Yu, H. T., & Peng, Z. R. (2019). Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression. Journal of Transport Geography, 75, 147–163. https://doi.org/10.1016/j.jtrangeo.2019.01.004
Zhang, X. A., Yang, F., Zhang, J., & Dai, Q. (2024). Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing. Land. https://doi.org/10.3390/land13040465
