Big data-based prediction of environmental impact on noise-vibration in South Korea

Abstract:

In Korea, due to the increase in development projects and the conflict between environment and development, the number of environmental impact assessments(EIA) is gradually increasing. In addition, the importance of using information related to EIA such as valuation of ecosystem services, 4th Industrial Revolution, and information support services is increasing. The EIA of the Republic of Korea began in 1971 and a large amount of data has been accumulated so far. Also, an information support system called EIASS is being established, and users can obtain EIA reports or related measurement information. Even if it is not included in the EIA, data such as residents' complaints and newspaper articles exist, and they can present problems in the process of conducting the EIA.

EIA usually indicates similar content and problems, with only differences depending on the characteristics of the project, type of project and surrounding environment. Therefore, if these information are systematically processed in big data form, similar content and problems will be identified when assessing new projects. Korea's EIA consists of a total of 21 items, and noise and vibration items are the most closely related to the lives of residents and have many complaints. The purpose of this study is to systematize EIA data for noise vibration items and to develop tools to evaluate new projects. In this study, big data techniques such as data mining and cluster analysis were used to collect, classify and systematize information on EIA. The accumulated EIA data will be classified and organized according to the geographical, spatial and environmental characteristics of the project. In evaluating new projects, similar types of assessment will be reviewed through the type of organized cases, which are expected to be utilized for assessment.

The development of these tools has three main effects. First, by being able to identify and supplement problems in the EIA stage in advance, it can prevent the establishment of erroneous predictions or reduction measures for EIA. Second, reducing the cost of mispredicting is possible to reduce social costs. Finally, it can have the function of resolving social conflicts by reducing complaints arising from proper EIA.