科研之友
微信
新浪微博
Facebook
分享链接
摘要
Noise maps are applied to assess noise level in cities all around the world. There are mainly two ways of producing noise maps: one way is producing noise maps through theoretical simulations with the surrounding conditions, such as traffic flow, building distribution, etc.; the other one is calculating noise level with actual measurement data from noise monitors. Currently literature mainly focuses on considering more factors that affect sound traveling during theoretical simulations and interpolation methods in producing noise maps based on measurements of noise. Although many factors were considered during simulation, noise maps have to be calibrated by actual noise measurements. Therefore, the way of obtaining noise data is significant to both producing and calibrating a noise map. However, there is little literature mentioned about rules of deciding the right monitoring sites when placed the specified number of noise sensors and given the deviation of a noise map produced with data from them. In this work, by utilizing matrix Gray Absolute Relation Degree Theory, we calculated the relation degrees between the most precise noise surface and those interpolated with different combinations of noise data with specified number. We found that surfaces plotted with different combinations of noise data produced different relation degrees with the most precise one. Then we decided the least significant one among the total and calculated the corresponding deviation when it was excluded in making a noise surface. Processing the left noise data in the same way, we found out the least significant datum among the left data one by one. With this method, we optimized the noise sensor's distribution in an area about 2km(2). And we also calculated the bias of surfaces with the least significant data removed. Our practice provides an optimistic solution to the situation faced by most governments that there is limited financial budget available for noise monitoring, especially in the undeveloped regions.
