Explaining the Optimal Model of Smart with Emphasis on Improving the Business Structure with the Neural Network Model in Yazd

Smart growth as a comprehensive strategy to counter the spread of sporadic and low-density areas around cities was discussed. The purpose of this study was to prioritize different areas of the research area and identify areas with intelligent potential in order to apply management strategies. To achieve this goal, 8 main variables of population density, per capita business use, use of counter offices, type of Internet and road network coverage, per capita residential, parking, bank were used.151 GPS points were taken to model and evaluate the validity of the model. Raster data layers to enter the model and neural network modeling was modeled Yazd smart prone areas. The research results showed regression above 70% in the training and testing phase. The area under the curve AUC validation model actually reflects the ability of the model to predict the dependent variable was 0.9769, which was very convenient. The parameters MSE, RMSE, R_Correlation_Test and R2 were also obtained as 0.0389, 0.1972, 0.8517 and 0.8912, respectively. Finally, the weight of the indicators and the dependent variables of the research were predicted. The results of the final map of area zoning capabilities in smart too low to 4013.78 showed that telling this story for intelligent infrastructure must be strengthened in this area. Areas with very high potential with an area of 687.31 have also been notable. By examining the modeling map and GPS points of Areas prone to smartening, a high compliance in the modeling was performed and the field points were observed.