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Using XAI to Select Pollutants of Total Pollutant Load management System (TPLMS) in Special Management coastal Zone (SMCZ) and Derive Marine Pollution Control Measures
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XAI를 활용한 특별관리해역의 연안오염총량관리제 오염물질 대상항목 선정 및 해양오염 관리 방안 도출

논문 기본 정보

Type
Academic journal
Author
Sesang Kim (부경대학교)
Journal
The Korean Society for Marine Environment & Energy Journal of the Korean Society for Marine Environment & Energy Vol.26 No.4 KCI Excellent Accredited Journal
Published
2023.11
Pages
396 - 412 (17page)
DOI
10.7846/JKOSMEE.2023.26.4.396

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Using XAI to Select Pollutants of Total Pollutant Load management System (TPLMS) in Special Management coastal Zone (SMCZ) and Derive Marine Pollution Control Measures
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The Ministry of Oceans and Fisheries reports on the status of special management coastal zone every year. Currently, Korea periodically utilizes the data from the marine environmental measurement network to identify the extent of marine pollution to establish and evaluate the total pollutant load management system for special management coastal zone. However, as a result of the operation of the marine environmental measurement network in 2016 by the Ministry of Oceans and Fisheries, most of the waters maintain relatively clean water quality with a water quality index (WQI) of 2 or higher. However, among the special management coastal zone, some peaks that are directly affected by the influx of land-based pollutants, such as the coast of Ulsan, the inner part of Masan Bay, the inner part of Sihwa Lake, the waters of the Nakdong River estuary, and the coast of Mokpo at the mouth of Yeongsan River, partially show results below WQI 4, indicating that measures to improve water quality are needed. This study identifies the current status of marine pollution sources in special management coastal zone through XAI (explainable artificial intelligence) analysis using data on special management coastal zone from the Marine Environmental Measurement Network, identifies the main pollution sources, and proposes a new direction for the decision-making problem of selecting pollutant targets for the total pollutant load management system. In addition, by deriving the impact of each pollutant and creating the marine pollution index, I was able to explore ways to establish policies such as selecting candidate sites for special management coastal zone in the future and derives future-oriented solutions to manage marine pollution. I was able to collect and refine marine environmental data affecting the marine environment, such as WQI, COD (chemical oxygen demand), and P (phosphorus), by time series, and analyze which marine pollutants have the most influence in the current special management coastal zone through XAI to propose a new direction for the decision making problem of selecting pollutant targets for the total pollutant load management system for each area. Also, Based on the marine pollution index, which reflects the impact of marine pollution sources, AI modeling to predict marine pollution one year from now was used to select candidate areas for future special management coastal zone and derive solutions for managing the selected special management coastal zone.
#Machine learning(머신 러닝) #Time series(시계열) #XAI(설명가능한 인공지능) #Marine pollution(해양오염) #Marine pollution index(해양오염지수) #Total coastal pollutiant load management system(연안오염 총량관리제) #Special management coastal(특별관리해역)

Contents

요약
Abstract
1. 서론
2. 연구 방법
3. 결과 및 고찰
4. 결론
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