Comparative reliability assessment of PET and UTCI thermal comfort indices using Monte Carlo simulation in urban microclimates

  • Broede, P. et al. The Universal Thermal Climate Index UTCI compared to ergonomics standards for assessing the thermal environment. Ind. Health 51, 16–24 (2013).

    Google Scholar 

  • Heidari, A., Davtalab, J. & Sargazi, M. A. Effect of awning on thermal comfort adjustment in open urban space using PET and UTCI indexes: A case study of Sistan region in Iran. Sustain. Cities Soc. 101, 105175 (2024).

    Google Scholar 

  • Jendritzky, G., De Dear, R. & Havenith, G. UTCI—why another thermal index?. Int. J. Biometeorol. 56, 421–428 (2012).

    Google Scholar 

  • Pantavou, K., Lykoudis, S., Nikolopoulou, M. & Tsiros, I. X. Thermal sensation and climate: a comparison of UTCI and PET thresholds in different climates. Int. J. Biometeorol. 62, 1695–1708 (2018).

    Google Scholar 

  • Jing, W., Qin, Z., Mu, T., Ge, Z. & Dong, Y. Evaluating thermal comfort indices for outdoor spaces on a university campus. Sci. Rep. 14, 21253 (2024).

    Google Scholar 

  • Heidari, A., Davtalab, J., Sargazi, M. A. & Piri, J. Machine Learning-Enhanced Assessment of Natural versus Artificial Shade Cooling Performance in Hot-Arid Climates: SVR Modeling with Hybrid GA-PSO Optimization. Build. Environ. 113789 (2025).

  • Piri, J. & Kisi, O. Hybrid non-linear probabilistic model using Monte Carlo simulation and hybrid support vector regression for evaporation predictions. Hydrol. Sci. J. 69, 2249–2277 (2024).

    Google Scholar 

  • Piri, J., Pirzadeh, B., Keshtegar, B. & Givehchi, M. Reliability analysis of pumping station for sewage network using hybrid neural networks-genetic algorithm and method of moment. Process Saf. Environ. Prot. 145, 39–51 (2021).

    Google Scholar 

  • Lynda, D., Logeswari, G., Tamilarasi, K. & Rakesh, S. Hybrid Bayesian deep learning model for predicting urban heat island intensity in African cities. Sci. Rep. 15, 31280 (2025).

    Google Scholar 

  • Deng, W., Wang, J., Yue, C., Guo, Y. & Zhang, Q. Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers. Energy and Build. 319, 114528 (2024).

    Google Scholar 

  • Gong, P., Huang, X., Huang, C. & Wang, S. in The International Conference on Computational Design and Robotic Fabrication. 273–283 (Springer).

  • Wang, C. & Leung, M.-Y. Effects of subjective perceptions of indoor visual environment on visual-related physical health of older people in residential care homes. Build. Environ. 237, 110301 (2023).

    Google Scholar 

  • Google. (Google, Mountain View, CA, 2024).

  • Mir, F., Khosravi, M. & Shoja, F. Assessment of optimal climatic comfort indices and future projections of heat stress in Zahedan: A strategic approach to climate change adaptation. Geography and Territorial Spatial Arrangement 15, 1–32 (2025).

    Google Scholar 

  • Höppe, P. The physiological equivalent temperature–a universal index for the biometeorological assessment of the thermal environment. Int. J. Biometeorol. 43, 71–75 (1999).

    Google Scholar 

  • Matzarakis, A. & Amelung, B. in Seasonal forecasts, climatic change and human health: health and climate 161–172 (Springer, 2008).

  • De Dear, R. J., Arens, E., Hui, Z. & Oguro, M. Convective and radiative heat transfer coefficients for individual human body segments. Int. J. Biometeorol. 40, 141–156 (1997).

    Google Scholar 

  • Zhang, S. A Practical Simulation Framework for Thermal Sensation Analysis of Fenestration Designs. (University of Toronto (Canada), 2020).

  • Psikuta, A. et al. Validation of the Fiala multi-node thermophysiological model for UTCI application. Int. J. Biometeorol. 56, 443–460 (2012).

    Google Scholar 

  • Bröde, P. et al. Deriving the operational procedure for the universal thermal climate index (UTCI). Int. J. Biometeorol. 56, 481–494 (2012).

    Google Scholar 

  • Li, J., Niu, J., Mak, C. M., Huang, T. & Xie, Y. Exploration of applicability of UTCI and thermally comfortable sun and wind conditions outdoors in a subtropical city of Hong Kong. Sustain. Cities Soc. 52, 101793 (2020).

    Google Scholar 

  • Teng, T.-P. & Chen, W.-J. Using Pearson correlation coefficient as a performance indicator in the compensation algorithm of asynchronous temperature-humidity sensor pair. Case Stud. Thermal Eng. 53, 103924 (2024).

    Google Scholar 

  • Knights, V. & Prchkovska, M. From equations to predictions: Understanding the mathematics and machine learning of multiple linear regression. J. Math. Comput. Appl 3, 1–8 (2024).

    Google Scholar 

  • Michaelides, P. G. in 21 Equations that Shaped the World Economy: Understanding the Theory Behind the Equations 29–44 (Springer, 2025).

  • Saunders, L. J., Russell, R. A. & Crabb, D. P. The coefficient of determination: what determines a useful R2 statistic?. Invest. Ophthalmol. Vis. Sci. 53, 6830–6832 (2012).

    Google Scholar 

  • Piri, J., Kahkha, M. R. R. & Kisi, O. Hybrid machine learning approach integrating GMDH and SVR for heavy metal concentration prediction in dust samples. Environmental Science and Pollution Research, 1–20 (2024).

  • Piri, J. & Kisi, O. Hybrid non-linear probabilistic model using Monte Carlo simulation and hybrid support vector regression for evaporation predictions. Hydrological Sciences Journal, 1–29 (2024).

  • Ran, Y., Wang, Z., Li, Y. & Xiao, F. in Building Simulation. 1179–1204 (Springer).

  • Sulaiman, H. & Olsina, F. Comfort reliability evaluation of building designs by stochastic hygrothermal simulation. Renew. Sustain. Energy Rev. 40, 171–184 (2014).

    Google Scholar 

  • Binarti, F., Koerniawan, M. D., Triyadi, S., Utami, S. S. & Matzarakis, A. A review of outdoor thermal comfort indices and neutral ranges for hot-humid regions. Urb. Climate 31, 100531 (2020).

    Google Scholar 

  • Blazejczyk, K., Epstein, Y., Jendritzky, G., Staiger, H. & Tinz, B. Comparison of UTCI to selected thermal indices. Int. J. Biometeorol. 56, 515–535 (2012).

    Google Scholar 

  • Matzarakis, A., Muthers, S. & Koch, E. Human biometeorological evaluation of heat-related mortality in Vienna. Theoret. Appl. Climatol. 105, 1–10 (2011).

    Google Scholar 

  • Mahmoud, A. H. A. Analysis of the microclimatic and human comfort conditions in an urban park in hot and arid regions. Build. Environ. 46, 2641–2656 (2011).

    Google Scholar 

  • Yan, H., Yang, L., Zheng, W., He, W. & Li, D. Analysis of behaviour patterns and thermal responses to a hot–arid climate in rural China. J. Therm. Biol 59, 92–102 (2016).

    Google Scholar 

  • Heidari, A. & Davtalab, J. Effect of Kharkhona on thermal comfort in the indoor space: A case study of Sistan region in Iran. Energy and Buildings 318, 114431 (2024).

    Google Scholar 

  • De Quadros, B., Pigliautile, I., Pisello, A., Krüger, E. & Mizgier, M. Reliability of urban microclimate simulations: spatio-temporal validation through intra-urban canyon transects for outdoor thermal comfort analysis. Int. J. Biometeorol. 68, 2715–2729 (2024).

    Google Scholar 

  • Bensoussan, A. in Optimal control and dynamic games: Applications in finance, management science and economics 311–317 (Springer, 2005).

  • Tang, J. W. et al. Comparison of the incidence of influenza in relation to climate factors during 2000–2007 in five countries. J. Med. Virol. 82, 1958–1965 (2010).

    Google Scholar 

  • Wijaya, H., Bandara, S., Rajeev, P., Gad, E. & Shan, J. Failure assessment of deteriorated steel light poles. Results in Eng. 23, 102621 (2024).

    Google Scholar 

  • Zhu, L. et al. Uncertainty and sensitivity analysis of cooling and heating loads for building energy planning. J. Build. Eng. 45, 103440 (2022).

    Google Scholar 

  • Manache, G. & Melching, C. S. Identification of reliable regression-and correlation-based sensitivity measures for importance ranking of water-quality model parameters. Environ. Model. Softw. 23, 549–562 (2008).

    Google Scholar 

  • Jingesi, M. et al. Association between thermal stress and cardiovascular mortality in the subtropics. Int. J. Biometeorol. 67, 2093–2106 (2023).

    Google Scholar 

  • Ghosn, M. et al. Reliability-based performance indicators for structural members. J. Struct. Eng. 142, F4016002 (2016).

    Google Scholar 

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