Modeling the Effects of Urban Green Spaces in Reduction of Noise Pollution using Neural Network Analysis (Case study: Isfahan city)

Noise pollution is one of the most important environmental problems in metropolitan areas that has serious challenges on mental and physical health. Noise reduction in many metropolitan areas such as Isfahan City is one of the major problems especially in historical city center. Green space with its ecological, social and economic functions has an important role in improving the life quality and has the ability to mitigate noise in urban areas. There is, however, lack of researches on the soundscape of urban green spaces in Iran. The aim of this study is modeling the effect of green spaces in reduction of noise pollution in Isfahan City using Neural Network Analysis (ANN), and providing map of sound in this area. 18 stations on the green spaces of Zayandehrood river’s sidelines and 3 urban parks were selected and their noise parameters and soundscape analysis were recorded twice a day (10 Am, and 5 PM) in summer and fall 2015. Measurements were performed by the Bruel and Kajaer 2230 Sound Level. Results showed that in the most stations, sound equivalent level in the morning was more than the evening. Tree and bushes density by a factor of importance 0.1362 and 0.0897 are the most effective factors. Also among the vehicles, cars with the impact factors of 0.1107 have more impress. Actually among the soundscape anthrophony with 0.0998 impact factor is most drastic. Noise mapping shows that noise pollution in fall is lower than summer. Generally results revealed that the recorded noise level is more than standard of Department of Environment in Iran, and existing green spaces should be increased in some stations o for mitigation of noise pollution.

Keywords:

Green spaces , Anthrophony , Soundscape , Traffic , Tree