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Dust is a phenomenon that has many environmental effects in various parts of human life, including: agriculture, economy, health and so on. The purpose of this study is to investigate and predict the dust phenomenon in Kermanshah. Meteorological data with a resolution of 3 hours in the statistical period (2020-2000) of Kermanshah station was obtained from the Meteorological Organization. First, the dust data were normalized and then using ANN neural network models to predict dust concentration and ANFIS adaptive neural network to debug and predict the time series of dust occurrence in MATLAB software were debugged and predicted. Findings showed that the maximum predicted dust concentration related to the minimum fenugreek point with the highest Pearson correlation with dust was estimated to be 3451.23 μg / m3. Also, the results of time series prediction using ANFIS model showed that the linear bell membership function with grade 3, in the training and testing stages, has the most desirable input function among other membership functions. According to the forecasting models, the highest probability of maximum dust occurrence in the next 20 years in Kermanshah was 94%.

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Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray Man software environment. Then UTCI and PMV index values were calculated using Bioklima software. The results showed that the most severe cold temperature stress on PMV index is in the winter and late autumn and UTCI index in January and February are the coldest stress. The power of neural networks, prediction of future performance network (generalized orientation) it simply is not possible and the new model presented in this paper have been restricted Boltzmann machine-based neural networks or neural networks is used deep belief. Using this structure, metrics Mean Squared Error (MSE) and mean absolute percentage error (MAPE) benchmark ate for seven indexes derived from data gathered by three factors related to the occurrence of weather conditions and other indicators of thermal comfort of human the system was evaluated. Assessment by dividing the data into training and testing parts, and the ratios have been of two-thirds, fifty percent and one-third And two benchmark MSE and MAPE were calculated. The proposed system performance in forecasting the human thermal comfort is desirable.