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A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services

Оглавление

Priyank Jain* and Gagandeep Kaur†

Dept. of CSE & IT, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

Abstract

Air pollution has become a major concern in many developing countries. There are various factors that affect the quality of air. Some of them are Nitrogen Dioxide (NO2), Ozone (O3), Particulate Matter 10 (PM10), Particulate Matter 2.5 (PM2.5), Sulfur Dioxide (SO2), and Carbon Monoxide (CO). The Government of India under the Open Data Initiative provides data related to air pollution. Interpretation of this data requires analysis, visualization, and prediction. This study proposes machine learning and visualization techniques for air pollution. Both supervised and unsupervised learning techniques have been used for prediction and analysis of air quality at major places in India. The data used in this research contains the presence of six major air pollutants in a given area. The work has been extended to study the impact of lockdown on air pollution in Indian cities as well.

Keywords: Open Data, JSON API, OpenAQ, clustering, SVM, LSTM, prediction, Heat Map visualizations

Machine Learning Algorithms and Applications

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