Neural network analysis of the functions of social participation in improving the environment

Background: The aim of this research was to investigate the effect of the participation functions in green space projects of Tehran Municipality on the development of the areas covered by these projects. Methods: The present study was an analytical cross-sectional design that 34 employees of municipal green spaces in Tehran were selected by random sampling method. Artificial neural network is a practical method for learning various functions. The questionnaire used in this study was developed by an extensive reviewing of the existing literature on the research topic by the researcher and using the opinions of the advisors and supervisor. Data collection was done using a questionnaire and analysis was done with SPSSWin16 and MATLAB. Results: There is a significant relationship between urban social functions, urban economic functions, urban environmental functions, urban institutional functions, rural social functions, participation. Urban environmental functions of participation in green space projects, urban institutional functions of participation in green space projects, and rural social functions of participation in green space projects in total account for 55% of the variable changes in the development of the covered areas. The use of artificial neural networks showed that the municipality's green space has been effective in urban and rural population changes. Conclusion: there is a significant relationship between the social, economic, environmental, institutional, and rural social functions of participation and the development of the covered areas, urban planning managers can use neural network to investigate the role of municipal green spaces in urban and rural population changes so that effective action can be taken.