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dc.contributor.authorKrittakom Srijiranonen_US
dc.contributor.authorNarissara Eiamkanitchaten_US
dc.contributor.authorSakgasit Ramingwongen_US
dc.contributor.authorKenneth Coshen_US
dc.contributor.authorLachana Ramingwongen_US
dc.description.abstractCoarse particulate matter (PM10), the inhalable particles with an aerodynamic diameter smaller than 10 micrometers are one of the major air pollutions that affect human health. Over the previous decade, a number of researchers applied various data mining techniques to create a temporal prediction model. This study reviews and discusses 100 research articles in computer science and environmental science coming from the Scopus database. The three processes of data mining techniques, including data preparation, model creation, and model evaluation for prediction PM10 are highlighted. A summary of the overall process directions of data mining as well as their output are revealed. Additionally, recommendations for future research are identified. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > Prediction.en_US
dc.subjectComputer Scienceen_US
dc.titleInvestigation of PM<inf>10</inf> prediction utilizing data mining techniques: Analyze by topicen_US
article.title.sourcetitleWiley Interdisciplinary Reviews: Data Mining and Knowledge Discoveryen_US
article.volume11en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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