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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