Industrialization and Urbanization of Indian Society

In the recent years, the industrialization and urbanization of Indian society has led to an increase in the concentration of pollutants in the atmosphere. transfer contamination is defined as a mixture of solid particles and gases in the air which has harmful and poisonous effects. Various experiments and studies have shown that long precondition exposure to such air defilement can lead to thoughtful health issues such as aggravated cardiovascular and respiratory illness, speed aging of lungs, diseases like asthma, bronchitis, cancer and a shortened life span. consort to the World Health Organization (WHO), over 12 million population die from environmental health risks annually. Air taint has become the fourth highest risk factor for premature deaths.Such degradation in the air grapheme levels has made air pollution a serious threat at a global level, especially for the developing countries, towards the sustainability of mankind. This has grabbed the attention of public as w ell as the government agencies.An air quality index (AQI) is a parameter use by the government agencies to communicate to the public how bemire the air quality currently is and how polluted it is forecast to become. As the AQI of a region increases, an increasingly large percentage of population of that area allow for experience adverse health effects.Several projects have been launched to combat air pollution in all major countries worldwide.For e.g. 1) Hebei Air Pollution Prevention and train Program (HAP- 201618) project in China to reduce the emissions of specific pollutants in Hebei 2) The Odd-Even Scheme implemented by the Indian Government in bailiwick capital Delhi (2016). There are ceaseless fighting efforts for air pollution reduction all around the world.As an endeavor on the physique of machine learning based air quality forecasting, this report presents an hatchway and algorithmic details of various statistical gravels in solving this dispute problem. The Machin e Learning models used in this study, to facilitate the prediction of pollutant concentrations, acknowledgeLinear regressionLogistic RegressionPolynomial regressionRandom Forest ClassificationDecision Tree RegressionDecision Tree ClassificationSupport Vector regressionSupport Vector ClassificationKNN ClassificationWe maneuver our air pollution forecast to the city of Delhi, India as it is at the headland for battling against air pollution. We focus on predicting the Air Quality Index (AQI) level of Delhi, as it is a quantitative method to profile air pollution level. In order to reduce the pollution levels in Delhi, we will be analyzing 5 pollutants and 5 other environment parameters responsible for increase in AQI levels. The fixed station data is taken for 3 stations viz. NSIT (Dwarka), RK Puram and Shadipur .ObjectivesCompare results of Air Quality Index (AQI) values obtained by distinct regression models and indeed propose the best model.Classify the dataset into 5 differ ent AQI categories, and then use Classification models to forecast the pollution category for next month.Analyze the most(prenominal) prominent pollutant, using Back Propagation, responsible for air pollution and apprize methods to control it.The rest of this paper is organized as follows Section II describes related work, and Section III provides background on data sources, participatory sensing systems and details the 5 regression and 5 classification models used in this study.Section IV describes the steps in our model, while model implementation and estimation accuracy is studied in Section V. The paper concludes in Section VI.