首页|Osaka University Researchers Publish New Study Findings on Machine Learning (Imp roving Machine Learning Based PM2.5 Prediction by Segregating Biomass Emission F actor from Chemical Transport Model)
Osaka University Researchers Publish New Study Findings on Machine Learning (Imp roving Machine Learning Based PM2.5 Prediction by Segregating Biomass Emission F actor from Chemical Transport Model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news originating from Osaka Univers ity by NewsRx correspondents, research stated, "Located in the heart of Mainland Southeast Asia, Thailand is associated with high biomass burning (BB) activitie s from local and neighbouring countries." The news editors obtained a quote from the research from Osaka University: "The seasonal pattern of BB manifests itself as a potential predictor for PM2.5 conce ntration. Consequently, we enhanced machine learning based PM2.5 prediction by s egregating BB factor from the Community Multiscale Air Quality (CMAQ). Two Light Gradient Boosting Machine (LightGBM) models with different CMAQ predictors were developed: the BB-integrated model, which incorporated CMAQ-simulated PM2.5 fro m all emission sources and the BB-segregated model, which incorporated CMAQ-simu lated PM2.5 from sources other than BB (CMAQ_PM25_Othr ) and CMAQ-simulated PM2.5 from BB emissions (CMAQ_PM25_ BB). The two models had shared control predictors, which included simulated mete orological variables from WRF model, population, elevation, and land-use variabl es, and they were evaluated using a crossvalidation (CV). The BB-segregated mode l outperformed the BB-integrated model, achieving overall-CV R2 values of 0.86 a nd 0.82, respectively. The analysis of feature importance for the BB-segregated model indicates that CMAQ_PM25_Othr and CMAQ_ PM25_BB are the two most significant predictors."
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