Forecasting Air Quality in Taiwan by Using Machine Learning.

Scientific Reports
Mike LeeShih-Hau Fang

Abstract

This study proposes a gradient-boosting-based machine learning approach for predicting the PM2.5 concentration in Taiwan. The proposed mechanism is evaluated on a large-scale database built by the Environmental Protection Administration, and Central Weather Bureau, Taiwan, which includes data from 77 air monitoring stations and 580 weather stations performing hourly measurements over 1 year. By learning from past records of PM2.5 and neighboring weather stations' climatic information, the forecasting model works well for 24-h prediction at most air stations. This study also investigates the geographical and meteorological divergence for the forecasting results of seven regional monitoring areas. We also compare the prediction performance between Taiwan, Taipei, and London; analyze the impact of industrial pollution; and propose an enhanced version of the prediction model to improve the prediction accuracy. The results indicate that Taipei and London have similar prediction results because these two cities have similar topography (basin) and are financial centers without domestic pollution sources. The results also suggest that after considering industrial impacts by incorporating additional features from the Taichung and Thong-...Continue Reading

References

Sep 16, 2008·The Science of the Total Environment·Ming-Tung ChuangChung-Te Lee
Jan 1, 2010·Journal of Ambient Intelligence and Smart Environments·Seun DeleaweDiane J Cook
Jan 17, 2012·Journal of Environmental Monitoring : JEM·Yu Tai-Yi
Jun 17, 2015·Environmental Science & Technology·Joshua S ApteMichael Brauer
May 24, 2017·Environmental Science & Technology·Xuefei HuYang Liu

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Citations

Nov 27, 2020·International Journal of Environmental Research and Public Health·Yuan-Chien LinJen-Kuo Tai
Jun 4, 2021·Scientific Reports·Sebastien Pérez Vasseur, José L Aznarte
Aug 25, 2021·Journal of Environmental Management·Hung-Hao ChangFeng-An Yang

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Methods Mentioned

BETA
feature extraction

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