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All issues Volume 198 (2024) SHS Web Conf., 198 (2024) 02004 References
Open Access
Issue
SHS Web Conf.
Volume 198, 2024
EduBIM2024 : Données, intelligences et nature de la ville durable
Article Number 02004
Number of page(s) 22
Section Conception assistée par les données
DOI https://doi.org/10.1051/shsconf/202419802004
Published online 11 October 2024
  1. GIEC (2022), Climate Change 2022: Impacts, Adaptation and Vulnerability, Intergovernmental Panel on Climate Change. [Google Scholar]
  2. I. Golasi, F. Salata, E. de Lieto Vollaro, M. Coppi (2018), Complying with the demand of standardization in outdoor thermal comfort: a first approach to the Global Outdoor Comfort Index (GOCI), Build. Environ., 130, pp. 104–119 https://doi.org/10.1016/j.buildenv.2017.12.021. [Google Scholar]
  3. P. Kumar, A. Sharma (2020), Study on importance, procedure, and scope of outdoor thermal comfort - A review, Sustain. Cities Soc., 61 (102297), https://doi.org/10.1016/j.scs.2020.102297. [Google Scholar]
  4. United Nations. Department of Economic and Social Affairs. Population Division, 2018. World Urbanization Prospects (2018) [Google Scholar]
  5. United Nations. Department of Economic and Social Affairs. Population Division, 2022. World Population Prospects (2022) [Google Scholar]
  6. C. Maftei, C. Buta, Application of thermal discomfort indices for the coastal zone of Black Sea, in Dobrogea Region, Ovidius Univ. Ann. Constanta - Ser. Civ. Eng., 19, pp. 87–100 (2017), https://doi.org/10.1515/ouacsce-2017-0008 [Google Scholar]
  7. D. Madhuranga, N. Perera (2019), Urban Heat Island Minimisation, Local Climate Zones and Parametric Optimisation: A “Grasshopper” Based Model, in: 5th International Conference on Counter Measures to Urban Heat Islands [Google Scholar]
  8. I.D. Stewart, T.R. Oke (2010), Thermal differentiation of local climate zones using temperature observations from urban and rural areas, in: AMS - 9th Symposium on the Urban Environment, 7 [Google Scholar]
  9. R. Wei, D. Song, N.H. Wong, M. Martin (2016), Impact of Urban Morphology Parameters on Microclimate, Procedia Eng., 169, pp. 142–149, https://doi.org/10.1016/j.proeng.2016.10.017. [Google Scholar]
  10. M. Yoshino (1990), Development of urban climatology and problems today, Energy Build., 15, pp. 1–10, https://doi.org/10.1016/0378-7788(90)90109-V. [CrossRef] [Google Scholar]
  11. ASHRAE (2020), ANSI/ASHRAE Standard 55 - Thermal Environmental Conditions for Human Occupancy, ASHRAE. [Google Scholar]
  12. M. Migliari, E. Briche, J. Despax, L. Chesne, O. Baverel (2024), A cross-analysis matrix comparing multi-site Local Climate Zone trends: Application to identify sustainable built morphologies improving summer daytime urban microclimate. Sustain. Futur., 7, pp. 1–21 https://doi.org/10.1016/j.sftr.2024.100162 [Google Scholar]
  13. M. Migliari, J. Despax, L. Chesne, O. Baverel (2023), Street albedos repartition’s effects on urban heat island and outdoor thermal comfort. UHI2023 - 6th Int. Conf. Countermeas to Urban Heat Islands, pp. 1–10 [Google Scholar]
  14. M. Migliari, J. Despax, A. Dogbo, L. Chesne, O. Baverel (2023), Caractérisation du comportement microclimatique et biométéorologique des complexes multicouches de sols pour différents matériaux de surface. CFGC, pp. 1–15 [Google Scholar]
  15. M. Taleghani, U. Berardi (2018), The effect of pavement characteristics on pedestrians’ thermal comfort in Toronto. Urban Clim., 24, pp. 449–459, https://doi.org/10.1016/j.uclim.2017.05.007 [Google Scholar]
  16. E. Erell, D. Pearlmutter, D. Boneh, P.B. Kutiel (2014), Effect of high-albedo materials on pedestrian heat stress in urban street canyons. Urban Clim., 10, pp. 367–386, https://doi.org/10.1016/j.uclim.2013.10.005 [Google Scholar]
  17. M. Migliari, R. Babut, C. De Gaulmyn, L. Chesne, O. Baverel (2022), The Metamatrix of Thermal Comfort: a compendious graphical methodology for appropriate selection of outdoor thermal comfort indices and thermo-physiological models for humanbiometeorology research and urban planning, Sustain. Cities Soc., 81, pp. 1–23, https://doi.org/10.1016/j.scs.2022.103852. [Google Scholar]
  18. Y. Wardeh, E. Kinab, G. Escadeillas, P. Rahme, S. Ginestet (2022), Review of the optimization techniques for cool pavements solutions to mitigate Urban Heat Islands. Build. Environ., 223, https://doi.org/10.1016/j.buildenv.2022.109482 [Google Scholar]
  19. M. Nikolopoulou, N. Baker, K. Steemers (2001), Thermal comfort in outdoor urban spaces: Understanding the human parameter. Solar Energy, 70, pp. 227–235, https://doi.org/10.1016/S0038-092X(00)00093-1 Nikolopoulou, [CrossRef] [Google Scholar]
  20. J. Sola, J. Sevilla (2004), Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science, 44, pp. 1464–1468 [Google Scholar]
  21. S.M. Lundberg, S.I. Lee (2017), A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst., 30, pp. 4765–4774 [Google Scholar]
  22. L. McInnes, J. Healy (2018), UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. ArXiv e-prints 1802.03426 [Google Scholar]
  23. L. McInnes, J. Healy (2013), Density-based clustering based on hierarchical density estimates. In: Advances in Knowledge Discovery and Data Mining, PAKDD2013, pp. 160–172 [Google Scholar]
  24. H.B. Rijal, M.A. Humphreys, J.F. Nicol (2019), Adaptive model and the adaptive mechanisms for thermal comfort in Japanese dwellings. Energy Build., 202 (109371) https://doi.org/10.1016/j.enbuild.2019.109371 [Google Scholar]
  25. M. Ester, H. Kriegel, J. Sander, X. Xu (1996), A density-based algorithm for discovering clusters in large spatial databases with noise. In: Knowledge Discovery and Data Mining, AAAI, pp. 226–231 [Google Scholar]

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