Open AccessComparative analysis of multiple classification models to improve PM10 prediction performance
Author(s) -
Yong-jin Jung,
Kyoung-Woo Cho,
Jongsung Lee,
Chang-Heon Oh
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i3.pp2500-2507
Subject(s) - logistic regression , decision tree , support vector machine , computer science , predictive modelling , logistic model tree , machine learning , artificial intelligence , random forest , ensemble learning , regression , particulates , decision tree learning , data mining , statistics , mathematics , ecology , biology
With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model.