PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing...
PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at hig...
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PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data
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TN_cdi_doaj_primary_oai_doaj_org_article_4cbb58fae9a7449a898c87b9ceedb64a
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4cbb58fae9a7449a898c87b9ceedb64a
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ISSN
2073-4433
E-ISSN
2073-4433
DOI
10.3390/atmos10070373
