A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network
Abstract
:1. Introduction
2. Data
2.1. The KEIDP Based on an AHP Approach
2.2. The EIA Big Data
3. Methods
3.1. Selection of the Target Development Projects
3.2. Normalization of EIA Big Data
3.3. Development and Validation of the KEIDP Calculation Model Using MLP-ANN
4. Results
4.1. The MLP-ANN Model Results
4.2. Application of the KEIDP Calculation Model
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Criteria | Sub-Criteria | Indicator | Equation | Weight |
---|---|---|---|---|
Ecological aspect | Ecosystem conservation | Phytomass | Phytomass before and after development | 0.074 |
Degree of green naturality over 6 grades | Ratio of green naturality over 6 grades before and after development | 0.139 | ||
Preservation of existing terrain | Topography change | Ratio of total earthwork volume and development area | −0.013 | |
Promotion of biodiversity | Green belt | Ratio of green belt area before and after development | 0.040 | |
Resource conservation aspect | Land water conservation | Rainwater storage basin | (Settling basin + detention pond)/development area | 0.011 |
Wastewater treatment | Capacity of wastewater treatment/(settled population + full-time employment) | 0.035 | ||
Waste generation | Amount of waste | Amount of waste during development and operation time/development area | −0.033 | |
Minimize fossil fuel use | Greenhouse gas emissions | Amount of greenhouse gas emission during development and operation time/development area | −0.020 | |
Amount of net production | Ratio of net production amount before and after development | 0.009 | ||
Amenity aspect | Resident protection | Noise pollution | Number of calmness facility/development area | −0.035 |
Atmospheric environmental material emissions | Emissions during operating time/development area | −0.027 |
Category | Number of Data | Used Data |
---|---|---|
Air quality | 5 | Particulate matter-10 (), nitrogen dioxide (ppm), sulfur dioxide (ppm), carbon monoxide (ppm), ozone (ppm) |
Water quality | 8 | Hydrogen ion concentration, dissolved oxygen (mg/L), suspended solids (mg/L), chemical oxygen demand (mg/L), biochemical oxygen demand (mg/L), total nitrogen (mg/L), total phosphorus (mg/L), total coliforms (MPN/100 mL) |
Soil quality | 8 | Cadmium (mg/kg), copper (mg/kg), arsenic (mg/kg), mercury (mg/kg), lead (mg/kg), zinc (mg/kg), nickel (mg/kg), fluorine (mg/kg) |
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Park, S.-H.; Jung, H.-S.; Lee, S.; Yoo, H.-S.; Cho, N.-W.; Lee, M.-J. A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network. Appl. Sci. 2021, 11, 7133. https://doi.org/10.3390/app11157133
Park S-H, Jung H-S, Lee S, Yoo H-S, Cho N-W, Lee M-J. A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network. Applied Sciences. 2021; 11(15):7133. https://doi.org/10.3390/app11157133
Chicago/Turabian StylePark, Sung-Hwan, Hyung-Sup Jung, Sunmin Lee, Heon-Seok Yoo, Nam-Wook Cho, and Moung-Jin Lee. 2021. "A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network" Applied Sciences 11, no. 15: 7133. https://doi.org/10.3390/app11157133
APA StylePark, S.-H., Jung, H.-S., Lee, S., Yoo, H.-S., Cho, N.-W., & Lee, M.-J. (2021). A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network. Applied Sciences, 11(15), 7133. https://doi.org/10.3390/app11157133