[논문]시정계 자료와 기계학습 기법을 이용한 지역 안개예측 모형 개발

김대하

doi:10.3741/jkwra.2021.54.12.1255

시정계 자료와 기계학습 기법을 이용한 지역 안개예측 모형 개발
Developing a regional fog prediction model using tree-based machine-learning techniques and automated visibility observations 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12, 2021년, pp.1255 - 1263

김대하 (전북대학교 토목환경자원에너지공학부)

초록
AI-Helper

안개는 대체수자원이 될 수 있으나 교통사고 위험을 높이고 공항 운영에 제약을 가하는 사회적 영향이 큰 기상현상이다. 본 연구에서는 1 km 미만 가시거리(시정)로 정의되는 안개 발생을 기상자료로 예측하는 지역 기계학습모형을 개발하고 그 예측력을 평가하였다. 전라북도 지역의 10개 기상청 지상관측소의 2017-2019년 시정 및 기상관측자료로 앙상블 분류기법인 Extreme Gradient Boosting (XGB), Light Gradient Boosting(LGB), Random Forests (RF)를 학습시켜 지역 안개 모형을 개발하였고 독립적인 2020년 자료로 모형의 사용성을 평가하였다. 그 결과, 학습·검증기간(2017-2019)에는 True Skill Score를 기준으로 가장 높은 예측력을 보인 방법은 LGB 기법이었지만 다른 두 모형에 비해 False Alarm Ratio가 컸다. RF 모형과 XGB 방법 역시 기존 연구에 상응하는 예측성능을 보이는 것으로 확인되었다. 2020년 자료를 입력해 안개 발생을 모의했을 때 세 모형의 예측성능은 2017-2019년 기간보다 떨어졌지만 모두 관측 안개일수의 공간분포와 일관되는 안개 위험을 예측했다. 세 기계학습모형은 안개위험이 상대적으로 높은 지역을 추출하는 기법으로 사용이 가능할 것으로 보인다.

Abstract ▼ AI-Helper

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

주제어

표/그림 (8)

그림 Fig. 2. (a) Monthly mean occurrences of foggy days, and (b) diurnal variations of fog occurrences at each stations during 2016-2020
그림 Fig. 1. The locations of the meteorological stations and mean annual foggy days for 2017-2020. The foggy days are defined as days with at least a single hourly record of visibility < 1 km. The numbers in the parentheses are the station identifiers of the Korea Meteorological Administration
표 Table 1. Performance metrics of RF, XGB, and LGB fog prediction models
그림 Fig. 3. True skill scores (POD minus POFD) at each station by (a) XGB, (b) LGB, and (c) RF
표 Table 2. Relative importance of predictors to fog occurrence forecasts for each model measured by information gain
그림 Fig. 4. Fog occurrence probabilities at each stations predicted by the (a) XGB, (b) LGB, and (c) RF
그림 Fig. 5. The distribution of foggy days in 2020. The definition of foggy days are the same as in Fig. 1
표 Table 3. Performance evaluation of fog predictions by XGB, LGB, and RF models for the year of 2020

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표제어: PCR

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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