A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere. Issue 11 (November 2020)

• Record Type: Journal Article
• Authors: Kumar, Saurabh; Mishra, Shweta; Singh, Sunil Kumar
• Is Part Of: Heliyon. Volume 6:Issue 11(2020)
• Publication Date: 2020-11
• Subjects: Computer science -- PM2.5 prediction -- Regression -- Atmospheric pollution -- Time series analysis -- Machine learning; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2020.e05618
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: During the last many years, the air quality of the capital city of India, Delhi had been hazardous. A large number of people have been diagnosed with Asthma and other breathing-related problems. The basic reason behind this has been the high concentration of life-threatening PM2.5 particles dissolved in its atmosphere. A good model, to forecast the concentration level of these dissolved particles, may help to prepare the residents with better prevention and safety strategies in order to save them from many health-related diseases. This work aims to forecast the PM2.5 concentration levels in various regions of Delhi on an hourly basis, by applying time series analysis and regression, based on various atmospheric and surface factors such as wind speed, atmospheric temperature, pressure, etc. The data for the analysis is obtained from various weather monitoring sites, set-up in the city, by the Indian Meteorological Department (IMD). A regression model is proposed, which uses Extra-Trees regression and AdaBoost, for further boosting. Experimentation for comparative study with the recent works is done and results indicate the efficacy of the proposed model. Abstract : Computer science; PM2.5 prediction; Regression; Atmospheric pollution; Time series analysis; Machine learning.