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RISS 인기검색어
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기계학습을 활용한 동아시아 지역의 TROPOMI 기반 SO<sub>2</sub> 지상농도 추정
최현영 ( Hyunyoung Choi ),강유진 ( Yoojin Kang ),임정호 ( Jungho Im ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.2
대기 중의 이산화황(SO<sub>2</sub>)은 주로 인위적 배출원에 의해 발생하며 화학 반응을 통해 (초)미세먼지를 형성하여 직간접적으로 주변 환경 및 인체 건강에 해로운 영향을 주는 물질이다. 특히 지상에서의 농도는 인간 활동과 밀접한 관련이 있어 모니터링의 필요성이 매우 크다. 따라서, 본 연구에서는 TROPOMI SO<sub>2</sub> 연직 컬럼 농도 산출물 및 타 위성 산물과 모델 산출물 등을 융합 활용하여 기계학습 기법에 적용하여 SO<sub>2</sub> 지상 농도 추정모델을 개발하였다. 기계학습 기법으로는 널리 활용되고 있는 RF(Random Forest)에 잔차 보정 과정을 결합한 2-step 잔차 보정 RF를 적용하였다. 개발된 모델은 무작위, 공간 및 시간별 10-fold 교차 검증을 통하여 검증하였으며, 기울기(slope) 값이 1.14-1.25, 상관계수(R) 값이 0.55-0.65, rRMSE 값이 약 58-63% 정도로 나타났다. 이는 잔차 보정이 적용되지 않은 기존의 RF 대비 slope의 경우 약 10%, R과 rRMSE의 경우 약 3% 가량 향상된 결과를 보인다. 국가별로 나누어 분석하였을 때에는 샘플 수가 적고 SO<sub>2</sub>의 전반적인 농도가 낮은 일본 지역에서의 공간별 10-fold 교차검증 성능이 소폭 감소하는 것으로 나타났다. SO<sub>2</sub> 지상농도 분포를 계절별로 표출하였을 때, 일본의 경우 다른 지역 대비 연중 저농도가 관찰되며 높은 결측 값 비율로 인하여 관측소 농도 대비 2-step 잔차 보정 RF 모델에서 과대 모의하는 경향이 관찰되었다. 대표적 고농도 발생지인 중국의 YRD(Yangtze River Delta) 와 한국의 SMA(Seoul Metropolitan Area)의 계절적 분포 변화를 추가적으로 분석하였을 때, 연료 연소로 인한 겨울철 농도 증가 패턴이 나타났다. 이는 인위적 배출원의 영향을 크게 받는 SO<sub>2</sub>의 시공간적인 분포 특성을 잘 반영하고 있는 결과이다. 따라서, 본 연구를 통하여 제안한 모델은 장기적으로 SO<sub>2</sub> 지상 농도의 시공간적 분포를 파악하는 데에 활용될 수 있을 것으로 기대된다. Sulfur dioxide (SO<sub>2</sub>) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO<sub>2</sub> concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)- derived column density data can provide spatially continuous monitoring of ground-level SO<sub>2</sub> concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO<sub>2</sub> concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO<sub>2</sub> produced by the model agreed with those of the in-situmeasurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO<sub>2</sub> concentrations on both the spatial and temporal domains.
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GOCI-II 대기상한 반사도와 기계학습을 이용한 남한 지역 시간별 에어로졸 광학 두께 산출
양세영,최현영,임정호,Seyoung Yang,Hyunyoung Choi,Jungho Im 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R<sup>2</sup>=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.
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의약품부작용보고원시자료를 활용한 와파린과 스타틴 동시복용의 약물 상호작용 실마리정보 탐색
원희현(Heehyun Won),최현영(HyunYoung Choi),박수빈(Suvin Park),최남경(Nam-Kyong Choi) 대한약학회 2023 약학회지 Vol.67 No.1
The increase in polypharmacy in elderly patients is recognized as a serious health, economic, and social problem. Warfarin and statins are commonly co-administered to cardiovascular disease patients. However, there is a potential risk of drug-drug interaction (DDI). The objective of this study was to detect signals of adverse events (AEs) for DDI after concomitant use of warfarin and statins using Korea adverse events reporting system (KAERS) database. KAERS database from January 2016 to December 2020 was used. Omega (Ω) shrinkage measure model and Chi-square statistics model were used to calculate the criteria for detecting AEs signal resulting from concomitant use of warfarin and statins. Three indices were used to define signals: Ω025 (frequentist) >0, Ω025 (Bayesian) >0, and >2. Detected signals were compared with AEs listed on the drug label in Korea, Micromedex, and SIDER for warfarin and statins, respectively. Twenty-seven AEs on reports of concomitant use were detected as signals. Of 27 detected signals, 11 signals, including breath odour not otherwise specified (NOS), depersonalization, gastrointestinal neoplasm NOS, pleural effusion, medical device complication, menopausal symptoms, oedema cerebral, osteitis, prostatic hyperplasia, lipoma, and skin hypertrophy, were not listed on drug label. We identified signals for concomitant use of warfarin and statins. Careful monitoring and further pharmacoepidemiological studies of DDI associated with new signals using other databases are needed.
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GOCI 위성영상과 기계학습 기법을 이용한 Ocean Colour-Climate Change Initiative (OC-CCI) Forel-Ule Index의 공간 상세화
성태준 ( Taejun Sung ),김영준 ( Young Jun Kim ),최현영 ( Hyunyoung Choi ),임정호 ( Jungho Im ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.5
Forel-Ule Index (FUI)는 자연에 존재하는 담수 및 해수의 색을 남색부터 고동색까지 21 가지의 등급으로 구분하는 지표이다. FUI는 여러 선행연구에서 수계의 부영양화 지수, 수질인자, 광 특성 등과 연관 지어 분석되었으며, 여러 수질인자의 광학적 정보를 동시에 가지고 있는 새로운 수질 지표로써의 가능성이 제시되었다. 본 연구에서는 500 m의 높은 공간해상도를 가지는 정지궤도 해양위성해색탑재체(Geostationary Ocean Color Imager; GOCI) 관측 자료와 Random Forest (RF) 기계학습 기법을 활용하여Ocean Colour-Climate Change Initiative (OC-CCI) 기반의 4 km FUI 자료를 공간 상세화 시켰다. 이를 활용하여 우리나라 연안 해역에 대한 수질인자와 의 상관관계와 주요 해역에 대한 FUI의 공간적 분포 및 계절별 특성 변화를 분석하였다. 검증 결과 RF 기법으로 추정한 RF FUI는 결정계수(R<sup>2</sup>)=0.81, 평균 제곱근 오차(Root Mean Square Error; RMSE)=0.7784로, Pitarch의 OC-CCI FUI 알고리즘을 적용하여 계산한 GOCI FUI 추정 정확도(R<sup>2</sup>=0.72, RMSE=0.9708) 대비 향상된 결과를 보였다. RF FUI는 총 질소(Total Nitrogen), 총 인(Total Phosphorus), 클로로필-a(Chlorophyll-a), 총 부유물질(Total Suspended Solids), 투명도(Secchi Disk Depth)를 포함하는 5가지 수질인자와 각각 0.87, 0.88, 0.97, 0.65, -0.98의 상관계수로 강한 상관성을 보였다. 산출된 FUI의 시간적 패턴 역시 여러 수질인자와의 물리적 관계를 반영하며 유의미한 계절적 패턴의 변화를 보였다. 본 연구의 결과로 한반도 연안 수질 관리에서 고해상도 FUI의 활용 가능성을 제시하였다. Forel-Ule Index (FUI) is an index which classifies the colors of inland and seawater exist in nature into 21 grades ranging from indigo blue to cola brown. FUI has been analyzed in connection with the eutrophication, water quality, and light characteristics of water systems in many studies, and the possibility as a new water quality index which simultaneously contains optical information of water quality parameters has been suggested. In this study, Ocean Colour-Climate Change Initiative (OC-CCI) based 4 km FUI was spatially downscaled to the resolution of 500 m using the Geostationary Ocean Color Imager (GOCI) data and Random Forest (RF) machine learning. Then, the RF-derived FUI was examined in terms of its correlation with various water quality parameters measured in coastal areas and its spatial distribution and seasonal characteristics. The results showed that the RF-derived FUI resulted in higher accuracy (Coefficient of Determination (R<sup>2</sup>)=0.81, Root Mean Square Error (RMSE)=0.7784) than GOCI-derived FUI estimated by Pitarch’s OC-CCI FUI algorithm (R<sup>2</sup>=0.72, RMSE=0.9708). RF-derived FUI showed a high correlation with five water quality parameters including Total Nitrogen, Total Phosphorus, Chlorophyll-a, Total Suspended Solids, Transparency with the correlation coefficients of 0.87, 0.88, 0.97, 0.65, and -0.98, respectively. The temporal pattern of the RF-derived FUI well reflected the physical relationship with various water quality parameters with a strong seasonality. The research findings suggested the potential of the high resolution FUI in coastal water quality management in the Korean Peninsula.
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