Author:
Senthivel Saminathan,Chidambaranathan Malathy
Abstract
Air Quality Index (AQI) is an indicator of the pollution level of our surroundings and household. Prediction of the AQI values from the historical values can help us analyze and mitigate the pollution levels. The AQI values can be classified into predetermined categories and machine learning algorithms can be made use of to improve the classification accuracy of the Air Quality Index value calculated. The main objective of the paper is to provide the potential researchers, with the importance of various Machine Learning approaches used for the forecast of the Air Quality Index. This paper analyzes various strategies used for the prediction, classification of AQI incorporating machine learning techniques. The air quality index can be calculated using Machine learning-based methods. Some of the methods to be considered are logistic regression, decision tree, support vector regression, support vector classifier, random forest tree, Naive Bayes classifier, and K-nearest neighbor. Application of these methods on the Air Quality Index datasets may yield different Accuracy, Recall, and F1 Score. Different algorithms that can be used for the said purpose with their strengths are summarized in a comparison table.
Publisher
International Information and Engineering Technology Association
Subject
Artificial Intelligence,Software
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献