| Title | Determining the influence of meteorological parameters on outdoor thermal comfort using ANFIS and ANN |
|---|---|
| ID_Doc | 41750 |
| Authors | Shah, R; Pandit, RK; Gaur, MK |
| Title | Determining the influence of meteorological parameters on outdoor thermal comfort using ANFIS and ANN |
| Year | 2023 |
| Published | Mausam, 74, 3 |
| Abstract | The study aims to develop artificial neural networks to predict outdoor thermal comfort using meteorological parameters as input parameters. Universal Thermal Climate Index (UTCI) is used as the target parameter. For this purpose, 5088 hours of field monitoring data were considered from four representative urban streets of Gwalior city, India. First, linear association was determined between meteorological parameters. Mean radiant temperature was to be in high correlation with globe temperature and surface temperature. Second, the Adaptive Neuro Fuzzy Inference System (ANFIS) was used to rank the meteorological parameters in order of their impact on UTCI. The air temperature was found to be having the strongest influence. Third, ANN models are developed to predict UTCI with air temperature as the only meteorological parameter in the input layer. The developed ANN models for all four streets show remarkable predictive ability for both the summer (R2 = 0.852, 0.986, 0.962, 0.955) and the winter season (R2 = 0.976, 0.870, 0.941, 0.950). Additionally, the success index of the developed models is found to be in range 0.73 - 1, 0.88 - 1, 0.86 - 1, 0.87 - 1 for the summer season and 0.78 - 0.99, 0.61 - 0.98, 0.55 - 0.98, 0.87 - 0.99 for the winter season. The study contributes to the smart city initiatives for future urban design by establishing that outdoor thermal comfort can be easily predicted using air temperature when other microclimatic parameters are difficult to record using a machine learning approach. |
| https://mausamjournal.imd.gov.in/index.php/MAUSAM/article/download/2976/5630 |
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