Determining the influence of meteorological parameters on outdoor thermal comfort using ANFIS and ANN

The study aims to develop artificial neural networks for prediction of outdoor thermal comfort using meteorological parameters as input parameters. Universal Thermal Climate Index (UTCI) is used the target parameter. For this purpose, a total number 5088 hours field monitoring data was considered from four representative urban streets Gwalior city, India. First, linear association determined between Mean radiant temperature be in high correlation with globe and surface temperature. Second, Adaptive Neuro Fuzzy Inference System (ANFIS) rank order their impact on UTCI. Air found having highest influence. Third, ANN models are developed predict UTCI air only parameter layer. all show remarkable predictive ability both summer (R2 = 0.852, 0.986, 0.962, 0.955) winter season 0.976, 0.870, 0.941, 0.950). Additionally, success index range 0.73 – 1, 0.88 0.86 0.87 1 0.78 0.99, 0.61 0.98, 0.55 0.99 season. contributes smart city initiatives future designing by establishing that can easily predicted when other microclimatic difficult record machine learning approach.