Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic

Coarse 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

Automated summary

This study reviews 100 research articles on the application of data mining techniques in predicting PM10, emphasizing the processes of data preparation, model creation, and model evaluation. The overall process directions and outputs of data mining are summarized, along with recommendations for future research.