[논문]앙상블 기반 모델을 이용한 서울시 PM2.5 농도 예측 및 분석

류민지; 손상훈; 김진수

doi:10.7780/kjrs.2022.38.6.1.16

[국내논문] 앙상블 기반 모델을 이용한 서울시 PM2.5 농도 예측 및 분석
Prediction and Analysis of PM2.5 Concentration in Seoul Using Ensemble-based Model 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.38 no.6 pt.1, 2022년, pp.1191 - 1205

류민지 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) , 손상훈 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) , 김진수 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공)

초록
AI-Helper

복잡하고 광범위한 원인을 가진 대기오염물질 중 particulate matter (PM)은 입자의 크기에 따라 분류된다. 그 중 PM_2.5는 그 크기가 매우 작아 사람이 흡입하면 인간의 호흡기나 심혈관에 질병을 유발할 수 있다. 이러한 위험에 대비하기 위해서는 국가 중심의 관리와 사전에 예방할 수 있는 모니터링 및 예측이 중요하다. 본 연구는 고농도 미세먼지의 발생이 잦은 서울시의 PM_2.5를 local data assimilation and prediction system (LDAPS) 기상 관련 인자 15가지와 aerosol optical depth (AOD), 화학인자 4가지를 독립변수로 하여 앙상블 모델 두 가지 random forest (RF)와 extreme gradient boosting (XGB)로 예측하고자 하였다. 예측에 사용된 두 모델의 성능 평가와 인자 중요도 평가를 수행하였으며, 계절별 모델 분석도 수행하였다. 예측 정확도 결과, RF가 R² = 0.85, XGB가 R² = 0.91의 높은 예측 정확도를 보이며 XGB가 RF보다 PM_2.5 예측에 적합한 모델임을 확인하였다. 계절별 모델 분석 결과, 봄에 농도가 높은 관측 값과 비교하여 예측 수행이 잘 되었다고 할 수 있다. 본 연구는 다양한 인자를 이용하여 서울시의 PM_2.5를 예측하였고, 좋은 성능을 보이는 앙상블 기반의 PM_2.5 예측 모델을 구축하였다.

Abstract ▼ AI-Helper

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R2 = 0.85 and XGB R2 = 0.91, and it was confirmed that XGB was a more suitable model for PM2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM2.5 of Seoul was predicted using various factors, and an ensemble-based PM2.5 prediction model showing good performance was constructed.

Keyword

표/그림 (10)

그림 Fig. 1. Study area and 40 AQMS in Seoul.
표 Table 1. Data used for predictors of PM_2.5
그림 Fig. 2. Raster data for AOD and LDAPS (26 February 2018).
그림 Fig. 3. Scatterplot of model performance: (a) RF train, (b) RF test, (c) XGB train, and (d) XGB test.
표 Table 2. A summary of model performance results
그림 Fig. 4. Feature importance (a) RF and (b) XGB.
그림 Fig. 5. Comparison of observed and predicted values: (a) comparison of spring observations and model predicts, (b) comparison of summer observations and model predicts, (c) comparison of fall observations and model predicts, and (d) comparison of winter observations and model predicts. Values are given as means.
그림 Fig. 6. RF prediction accuracy as a seasonal scatterplot: (a) Spring RF model performance Scatterplot,(b) Summer RF model performance scatterplot, (c) Fall RF model performance scatterplot, and(d) Winter RF model performance scatterplot.
그림 Fig. 7. XGB prediction accuracy as a seasonal scatterplot: (a) Spring XGB model performance scatterplot, (b) Summer XGB model performance scatterplot, (c) Fall XGB model performance scatterplot, and (d) Winter XGB model performance scatterplot.
표 Table 4. Seasonal analysis result

AI 본문요약
AI-Helper

대상 데이터

두 데이터는 대한민국이 위치한 픽셀의 해당 값을 AQMS 위치 데이터를 기반으로 지상 관측 인자에 join 하여 최종 데이터 셋을 구축하였다. 모든 입력 데이터를 AQMS 지점에 매칭한 후 25개의 인자 중 결측 값이 하나라도 포함되는 날의 데이터는 모두 제거하였고 최종 데이터는 총 15,300개로 구축되었다.
데이터셋은 XGB 모델에서 train_test_split 기능으로 분할하여 RF와 XGB에 동일한 train, test 데이터셋을 적용하였다. 최종적으로 train 12,240개 test 3,060개로 분할된 데이터셋을 이용하여 모델의 훈련과 검증을 수행하였다.

데이터처리

구축된 모델의 훈련과 검증을 위하여 데이터를 train과 test로 분할하였다. 데이터셋은 XGB 모델에서 train_test_split 기능으로 분할하여 RF와 XGB에 동일한 train, test 데이터셋을 적용하였다.
Grid-Search CV 기법은 사용자가 직접 모델의 하이퍼 파라미터의 값을 리스트로 입력하면 값에 대한 경우의 수마다 예측 성능을 평가하고 비교하며 최적의 파라미터 값을 찾는 과정을 진행하는 방식이다. RF에 5-fold CV와 Grid-search CV를 적용해 RF의 n_estimators에 대한 파라미터 최적화 과정을 거쳐 최종 RF 모델 구축을 수행하였다. XGB 또한 K-fold CV와 Grid-search CV를 적용하였다.

이론/모형

RF 모델의 과적합 방지와 모델의 정확도 향상을 위한 파라미터 최적화 과정에서 k-fold cross validation (K-fold CV) 기법과 grid-search cross validation (Grid-search CV) 기법을 적용하였다. K-fold CV는 데이터를 k개로 분할하고 k개의 모델을 만들어 k-1개의 분할에서 훈련하고 나머지 분할에서 평가하는 방법이다.
XGB 또한 K-fold CV와 Grid-search CV를 적용하였다. XGB는 조정할 수 있는 파라미터의 종류가 매우 다양해서 조정의 범위가 넓었고 본 연구에서는 10-fold CV와 Grid-Search CV 기법을 적용하여 subsample, max_depth, colsample_bytree, learning_rate, nthread, n_estimators, min_child_weight의 하이퍼 파라미터를 선정하여 최종 모델을 구축하였다.

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동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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