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Prediction of Daily PM10 Concentration for Air Korea Stations Using Artificial Intelligence with LDAPS Weather Data, MODIS AOD, and Chinese Air Quality Data 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.36 no.4, 2020년, pp.573 - 586  

Jeong, Yemin (Department of Spatial Information Engineering, Pukyong National University) ,  Youn, Youjeong (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) ,  Cho, Subin (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) ,  Kim, Seoyeon (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) ,  Huh, Morang (Nano Weather Incorporation) ,  Lee, Yangwon (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)

Abstract ▼ AI-Helper 아이콘AI-Helper

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the predi...

주제어

#Air pollution   #PM10   #Artificial intelligence  

표/그림 (16)

AI 본문요약
AI-Helper 아이콘 AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

제안 방법

  • We gathered the 3 million records for the hourly PM10 concentration from the 331 Air Korea stations. Also, the daily PM10 concentration data for three Chinese cities, the MODIS (Moderate Resolution Imaging Spectroradiometer) AOD (aerosol optical depth) images, and the LDAPS (Local Data Assimilation and Prediction System) meteorological variables were obtained to construct a matchup database. We built an RF model for the prediction of the PM10 concentration of tomorrow for the 331 Air Korea stations.
  • The RF model included 11 input variables: today’s PM10 concentration for each station, today’s PM10 concentration of Beijing, Tianjin, and Weihai, today’s MODIS AOD, and air temperature, relative humidity, wind speed, and boundary layer height for today and tomorrow.
  • We carried out 10-fold cross-validation for the blind test to evaluate the accuracy of our RF model (Fig. 4). First, the 230,639 matchups were divided into ten groups by random sampling.

데이터처리

  • The 10-fold cross-validation enables a more stable model by using the training data with a less-biased sampling. The indices such as MBE (mean bias error), MAE (mean absolute error), RMSE (root mean square error), NRMSE (normalized mean square error), and CC (correlation coefficient) were used for the validation statistics.
본문요약 정보가 도움이 되었나요?

참고문헌 (31)

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