1. Galal, O. M.; Sailor, D. J.; Mahmoud, H. The impact of urban form on outdoor thermal comfort in hot arid environments during daylight hours, case study: New Aswan. Building and Environment. 2020;184:107222. DOI: 10.1016/j.buildenv.2020.107222
2. Jia, S.; et al. A hybrid framework for assessing outdoor thermal comfort in large-scale urban environments. Landscape and Urban Planning. 2025;256:105281. DOI: 10.1016/j.landurbplan.2024.105281
3.Jing, W.; et al. Evaluating thermal comfort indices for outdoor spaces on a university campus. Scientific Reports. 2024;14(1):21253. DOI: 10.1038/s41598-024-71805-5
4. Hataminejad, H.; Arvin, M.; Mohammadivanani, A.; Bzrafkan, S. Analysis of the spatial crimes dispersion in urban parks (case study: Parks Tehran city). Strategic Research on Social Problems. 2017;6(2):89-104. DOI: 10.22108/ssoss.2017.22146
5.Ahmadi, M.; Dadashi, A. The Identification of Urban Thermal Islands based on an Environmental Approach, Case Study: Isfahan Province. Geography and Environmental Planning. 2017;28(3):1-20. DOI: 10.22108/gep.2017.98318.0
6.Megri, A. C.; El Naqa, I. Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions. Indoor and Built Environment. 2014;25(1):6-16. DOI: 10.1177/1420326X14539693
7. Veisi, O.; Attarhay Tehrani, A.; Gharaei, B.; Shakibamanesh, A. Using Artificial Intelligence for Predicting Universal Thermal Climate Index Based on Different Urban Conditions: A Comparative Study of Machine Learning Models. SSRN. DOI: 10.2139/ssrn.4840700
8. Kuzmanović, D.; Banko, J.; Skok, G. Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing. International Journal of Biometeorology. 2024;68(5):965–977. DOI: 10.1007/s00484-024-02640-6
9. Shah, R.; Pandit, R.; Gaur, M. Urban physics and outdoor thermal comfort for sustainable street canyons using ANN models for composite climate. Alexandria Engineering Journal. 2022;61(12):10871–10896. DOI: 10.1016/j.aej.2022.04.024
10. Briegel, F.; et al. High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning. Geoscientific Model Development. 2024;17(4):1667–1688. DOI: 10.5194/gmd-17-1667-2024
11. Yang, Z.; et al. GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022. Earth System Science Data. 2024;16(5):2407–2424. DOI: 10.5194/essd-16-2407-2024
12. Zhong, G. Convolutional Neural Network Model to Predict Outdoor Comfort UTCI Microclimate Map. Atmosphere. 2022;13(11):1860. DOI: 10.3390/atmos13111860
13. Liu, T.; et al. Outdoor Thermal Comfort Research and Its Implications for Landscape Architecture: A Systematic Review. Sustainability. 2025;17(5):2330. DOI: 10.3390/su17052330
14. Anders, J.; et al. Simplifying heat stress assessment: Evaluating meteorological variables as single indicators of outdoor thermal comfort in urban environments. Building and Environment. 2025;274:112658. DOI: 10.1016/j.buildenv.2025.112658
15. Ahmadi, M. M.; Amini Zadeh, B.; Aqamalai, R. Thermal performance assessment of urban fabric in Tehran climate: lessons for climate-responsive urban design. Journal of Fine Arts: Architecture and Urbanism. 2020;25(1):5–15. DOI: 10.22059/jfaup.2020.296175.672397
16. Lee, H.; Park, S.; Mayer, H. Approach for the vertical wind speed profile implemented in the UTCI basics blocks UTCI applications at the urban pedestrian level. International Journal of Biometeorology. 2025;69(3):567–580. DOI: 10.1007/s00484-025-02915-6
17. Entezari, A.; Mayvaneh, F.; Rezaie, K.; Rahimi, F. An adaptive estimation method to predict thermal comfort indices using deep belief neural networks. Journal of Geographic Sciences (JGS). 2018;18(51):23–40. DOI: 10.29252/jgs.18.51.23
18. Solcast. Available from: https://solcast.com/.
19. Palma, W. Time Series Analysis. John Wiley & Sons; 2016.
20. Han, J.; Kamber, M.; Pei, J. Data Mining: Concepts and Techniques. Waltham: Morgan Kaufmann Publishers; 2012.