A Study on the Improvement of Legal System for Strengthening the Utilization of Spatial Information in Environmental Impact Assessment

Cho, Nam-Wook; Han, Hyeon-Gyeong; LeeMoungJin,

doi:10.15301/jepa.2020.28.3.159

Cited by 1 publication

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“…In South Korea, various EIA methods have been introduced and implemented to prevent environmental problems that may arise with development projects [16][17][18][19][20][21]. In addition, after the development projects were completed, post-environmental impact investigations were conducted to validate the effectiveness of the EIA systems.…”

Section: Introductionmentioning

confidence: 99%

A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network

et al. 2021

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In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and consist of indicators related to air, soil, and water that are measured before and after the development project. The impact of the development project on the environment can be evaluated through the variations of each indicator. This study analyzed trends in the environmental impacts of development projects during 2007–2016 using 21 types of EIA Big Data. A model was developed to estimate the Korean Environment Institute’s Environmental Impact Assessment Index for Development Projects (KEIDP) using a multi-layer perceptron-based artificial neural network (MLP-ANN) approach. A trend analysis of development projects in South Korea revealed that the mean value of KEIDP gradually increased over the study period. The rate of increase was 0.007 per year, with an R2 value of 0.8. In the future, it will be necessary for all management agencies to apply the KEDIP calculation model to minimize the impact of development projects on the environment and reduce deviations among development projects through continuous monitoring.

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Section: Introductionmentioning

confidence: 99%