A Comprehensive Study to Forecast the Delhi and Bangalore Cities Air Pollution using Machine Learning Models

Darekar, Parth (2021) A Comprehensive Study to Forecast the Delhi and Bangalore Cities Air Pollution using Machine Learning Models. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration manual) Download (1MB) | Preview

Abstract

Air pollution is now a significant research area of topic in recent years, due to its detrimental implications. In today’s environment, it is also recognized as one of the primary danger factors. In the existence of air pollution management systems, the first step is accurate air quality measurement, which contributes in the economic and social development of industrialized countries. For both systematic emissions management and public health and well-being, accurate air quality forecasting is critical. Delhi, India’s capital, as well as Bangalore one of Information Technology hub of the country has been the world’s most polluted city for the past two years. Different time series models, such as ARIMA (Autoregressive Integrated Moving Average) and SES (Simple Exponential Smoothing), have been effectively utilised in the past to forecast air pollution. Taking into consideration previous study as well as to anticipate on the future problems regarding air pollution, different time series models like SARIMA (Seasonal Autoregressive Integrated Moving Average) VAR (Vector Auto Regressive) VARMA (Vector Auto-Regressive Moving Average), ARFIMA(Auto Regressive Fractionally Integrated Moving Average), with the help of neural network model LSTM, have been used in this research study to forecast the Bangalore and Delhi cities air pollutants. This models can discover underlying trends, time series data analysis as well as can assist us in dealing with the problems regarding air pollution.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Clara Chan
Date Deposited:	18 Nov 2021 10:15
Last Modified:	18 Nov 2021 10:15
URI:	https://norma.ncirl.ie/id/eprint/5144

Actions (login required)

View Item