The world we live in is being rapidly automated and emerging technologies like Cloud, Internet of Things, and so forth are being continuously integrated into concepts such as Smart Cities to provide a high level of comfort to the residents with minimum human intervention [10]. A major challenge faced by corporations of developed cities is to control and regulate air quality. With the advent of modern air quality monitoring and pollution control systems, a novel prediction framework aids the process of finding effective solutions to complex problems. This project focuses on investigating the correlation between air quality and weather and building a prediction model based on the results of the exploratory analysis of historical weather and pollution data. Air quality is assessed based on a banding system which measures the levels of pollutants, namely Ozone (O3), Nitrogen dioxide (NO2) and Particulate matter - PM10 and PM2.5. The overall air quality index at any particular time is given as the maximum band for any pollutant. PM2.5 is fine particulate matter of size less than 2.5 micrometers and is considered to have adverse impacts on health ranging from lung cancer to cardiovascular diseases. Although PM2.5 is a crucial factor in deciding the overall air quality index, it is currently not included as a pollutant in the UK air quality banding system issued by the Committee on the Medical Effects of Air Pollutants (COMEAP). This is because extensive monitoring of PM2.5 levels using dedicated instruments has only started since 2015 and the presently available data is insufficient for conclusive analysis. [1] This project aims to predict the air quality band for PM2.5 using present and historical pollution data in combination with predicted weather data which is readily available. To solve this problem, firstly, exploratory data analysis will be conducted on available weather and pollution datasets to discover the correlation between different features. After employing suitable data cleaning and feature engineering methods based on the observations made, the feasibility of using different machine learning techniques such as classification and regression models will be analyzed.
Cloud, Internet of Things, WHO, ANN, CNN, LSTM.
International Journal of Trend in Scientific Research and Development - IJTSRD having
online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International
Journal which provides rapid publication of your research articles and aims to promote
the theory and practice along with knowledge sharing between researchers, developers,
engineers, students, and practitioners working in and around the world in many areas
like Sciences, Technology, Innovation, Engineering, Agriculture, Management and
many more and it is recommended by all Universities, review articles and short communications
in all subjects. IJTSRD running an International Journal who are proving quality
publication of peer reviewed and refereed international journals from diverse fields
that emphasizes new research, development and their applications. IJTSRD provides
an online access to exchange your research work, technical notes & surveying results
among professionals throughout the world in e-journals. IJTSRD is a fastest growing
and dynamic professional organization. The aim of this organization is to provide
access not only to world class research resources, but through its professionals
aim to bring in a significant transformation in the real of open access journals
and online publishing.