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K-Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic

机译:K-Meation Covid-19流行病锁定时期北方北方北方北方北方的环境空气质量数据的聚类

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The analysis of the lockdown effect during covid-19 pandemic on ambient quality of air of Uttarakhand state of India, has been performed. The combination of SO2, NO2, and particulate matter (P.M.10) indicates ambient air quality characteristics. The clustering capability of the K-means clustering technique is investigated with two different approaches of measuring distance using MATLAB. The first approach is termed Euclidean distance and the second one is cosine distance. The data, which is clustered, is the air uualitv data containing three major components of air pollution such as P.M.10, SO2, and NO2 of different major cities of Uttarakhand.
机译:已经进行了分析在印度北方印度的北方北方空气环境中的Covid-19流行病中的锁定效应。所以的组合 2 , 不 2 和颗粒物质(下午10分)表示环境空气质量特征。通过使用MATLAB的两种不同的测量距离方法研究了K-Means聚类技术的聚类能力。第一种方法被称为欧几里德距离,第二个方法是余弦距离。集群的数据是空气污染三个主要成分的空气污染,如下午10点,所以 2 , 和不 2 Uttarakhand的不同主要城市。

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