A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network
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...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/7133 |
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| author | Sung-Hwan Park Hyung-Sup Jung Sunmin Lee Heon-Seok Yoo Nam-Wook Cho Moung-Jin Lee |
| author_facet | Sung-Hwan Park Hyung-Sup Jung Sunmin Lee Heon-Seok Yoo Nam-Wook Cho Moung-Jin Lee |
| author_sort | Sung-Hwan Park |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T09:17:42Z |
| format | Article |
| id | doaj.art-424b260ba30d4966b596355169508593 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:17:42Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-424b260ba30d4966b5963551695085932023-11-22T05:24:56ZengMDPI AGApplied Sciences2076-34172021-08-011115713310.3390/app11157133A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural NetworkSung-Hwan Park0Hyung-Sup Jung1Sunmin Lee2Heon-Seok Yoo3Nam-Wook Cho4Moung-Jin Lee5Marine Disaster Research Center, Korea Institute of Ocean Science and Technology, Busan 49111, KoreaDepartment of Geoinformatics, University of Seoul, Seoul 02504, KoreaEnvironmental Assessment Group, Korea Environment Institute, Sejong 30147, KoreaEnvironmental Assessment Group, Korea Environment Institute, Sejong 30147, KoreaEnvironmental Assessment Group, Korea Environment Institute, Sejong 30147, KoreaCenter for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, KoreaIn 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2076-3417/11/15/7133environmental impact assessmentEIA Big Datadevelopment project monitoringartificial neural networkKorean Environment Institute |
| spellingShingle | Sung-Hwan Park Hyung-Sup Jung Sunmin Lee Heon-Seok Yoo Nam-Wook Cho Moung-Jin Lee A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network Applied Sciences environmental impact assessment EIA Big Data development project monitoring artificial neural network Korean Environment Institute |
| title | A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network |
| title_full | A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network |
| title_fullStr | A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network |
| title_full_unstemmed | A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network |
| title_short | A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network |
| title_sort | trend analysis of development projects in south korea during 2007 2016 using a multi layer perceptron based artificial neural network |
| topic | environmental impact assessment EIA Big Data development project monitoring artificial neural network Korean Environment Institute |
| url | https://www.mdpi.com/2076-3417/11/15/7133 |
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