[논문]기계학습을 통한 토양오염물질 농도 예측 및 분포 매핑

강혜원; 박상진; 이동근

doi:10.14249/eia.2022.31.4.214

기계학습을 통한 토양오염물질 농도 예측 및 분포 매핑
Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms 원문보기

환경영향평가 = Journal of environmental impact assessment, v.31 no.4, 2022년, pp.214 - 225

강혜원 (서울대학교 농업생명과학대학 생태조경.지역시스템공학부) , 박상진 (서울대학교 환경대학원 협동과정 조경학 및 대학원 융합전공 스마트시티 글로벌 융합) , 이동근 (서울대학교 농업생명과학대학 조경.지역시스템공학부)

초록
AI-Helper

본 연구는 사업시행이 환경에 미치는 부정적 영향을 최소화할 수 있는 방안을 강구하기 위해 환경영향평가 토양 부문을 강조하였다. 영향평가 절차에 대한 일련의 노력으로서 도시개발사업을 대상으로 하는 국가 인벤토리 기반 데이터베이스를 구축하였으며, 세 가지 기계학습 모델 성능 평가 및 토양오염물질 농도분포 매핑을 진행하였다. 여기에서, 가장 우수한 성능을 보여준 Random Forest 모델을 사용하여 대한 민국 수도권 지역을 대상 9가지 토양오염물질을 매핑하였다. 본 연구의 결과는 도시화가 가장 활발한 서울지역에서 아연(Zn), 불소(F) 및 카드뮴(Cd) 농도가 상대적으로 우려되는 것을 발견하였다. 또한, 수은(Hg)과 크롬(Cr6+)의 경우 농도가 기준 이하로 검출되었는데, 이는 중금속 농도에 영향을 미치는 산업 및 공업단지와 같은 오염원 부족이 원인으로 도출되었다. 토양오염물질 공간분포 매핑을 통해 토양특성 및 토지이용 유형과 오염물질 간의 유의한 상관관계를 유추하였다. 이를 통해 사업 현장 위치에 관한 토양오염 최소화 및 계획 결정에 대한 효율적인 토양관리 방안을 구축할 수 있을 것으로 기대한다.

Abstract ▼ AI-Helper

This study emphasized the soil of environmental impact assessment to devise measures to minimize the negative impact of project implementation on the environment. As a series of efforts for impact assessment procedures, a national inventory-based database was established for urban development projects, and three machine learning model performance evaluation as well as soil pollutant concentration distribution mapping were conducted. Here, nine soil pollutants were mapped to the metropolitan area of South Korea using the Random Forest model, which showed the best performance. The results of this study found that concentrations of Zn, F, and Cd were relatively concerned in Seoul, where urbanization is the most active. In addition, in the case of Hg and Cr6+, concentrations were detected below the standard, which was derived from a lack of pollutants such as industrial and industrial complexes that affect contents of heavy metals. A significant correlation between land cover and pollutants was inferred through the spatial distribution mapping of soil pollutants. Through this, it is expected that efficient soil management measures for minimizing soil pollution and planning decisions regarding the location of the project site can be established.

주제어

표/그림 (9)

표 Table 1. Literature review related to the soil pollutants analysis
그림 Figure 1. Location of the study area and the distribution of sampling points.
그림 Figure 2. Research flow for the prediction of soil pollutants.
표 Table 2. Descriptive statistics of soil pollutants of the literature review
표 Table 3. List of input data
표 Table 4. Mean value by soil pollutants (Units: mg/kg)
그림 Figure 3. Comparison of performance of each machine learning algorithms (NB, kNN, RF) for pollutants in possibility of soil contamination (The red dotted line: the average AUC value of each machine learning algorithms).
표 Table 5. Validation results of the machine learning algorithms with average AUC value 8 repetitions of model training
그림 Figure 4. Spatial prediction of each heavy metals and F in soil.

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