Comparative Analysis of Supervised Machine Learning Techniques for AQI Prediction

A. Pant, Sanjay Sharma, M. Bansal, Mandeep Narang
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引用次数: 4

Abstract

Air pollution is a significant challenge in a populated area. This paper focuses on predicting air quality index using supervised machine learning techniques in the capital city of Uttarakhand state, India, i.e., Dehradun based on the available pollutants (PM10, PM2.5, SO2, NO2). The result shows that the decision tree classifier is more accurate, with an accuracy of 98.63%. In contrast, the logistic regression is the least one with an accuracy of 91.78% for air quality prediction. The study also finds that the AQI level is low in May due to high temperatures. The study also finds that the Himalayan drugs-ISBT area is in the poor range of AQI for the capital city of Uttarakhand state.
有监督机器学习技术在空气质量预测中的比较分析
在人口稠密的地区,空气污染是一个重大挑战。本文的重点是基于可用污染物(PM10, PM2.5, SO2, NO2),使用监督机器学习技术预测印度北阿坎德邦首府德拉敦的空气质量指数。结果表明,决策树分类器的准确率达到了98.63%。logistic回归的预测精度最低,为91.78%。研究还发现,由于高温,5月份空气质量指数较低。该研究还发现,喜马拉雅毒品- isbt地区在北阿坎德邦首府的空气质量指数中处于较差的范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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