논문 상세보기
pp.269-285
Because of the population growth and industrialization in recent decades, the air quality over the world has been worsened with the increase of PM10 concentration. Korea is located near the eastern part of China which has many industrial complexes, so the consideration of China’s air quality is necessary for the PM10 prediction in Korea. This paper examines a machine learning-based modeling of the prediction of tomorrow’s PM10 concentration in the form of a gridded map using the AirKorea observations, Chinese cities’ air quality index, and NWP (numerical weather prediction) model data. A blind test using 23,048 cases in 2019 produced a correlation coefficient of 0.973 and an MAE (mean absolute error) of 4.097㎍/㎥, which is high accuracy due to the appropriate selection of input variables and the optimization of the machine learning model. Also, the prediction model showed stable predictability irrespective of the season and the level of PM10. It is expected that the proposed model can be applied to an operative system if a fine-tuning process using a larger database is accomplished.
1. 서론
2. 연구 자료
1) 지상관측 자료
2) 기상예보모델 자료
3. 기계학습 예측모델
1) 개요
2) 예측모델의 학습 및 시험
3) 익일 PM10 예측지도 산출
4. 결과 및 토의
1) 예측모델의 학습 결과
2) 예측모델의 시험 결과
3) 익일 PM10 격자자료 산출
5. 결론
References
