Research Article
A Geo-Based Fine Granularity Air Quality Prediction Using Machine Learning and Internet-of-Things
@INPROCEEDINGS{10.1007/978-3-319-73564-1_29, author={Hang Wang and Yu Sun and Qingquan Sun}, title={A Geo-Based Fine Granularity Air Quality Prediction Using Machine Learning and Internet-of-Things}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Machine learning Air quality prediction Internet-of-Things}, doi={10.1007/978-3-319-73564-1_29} }- Hang Wang
Yu Sun
Qingquan Sun
Year: 2018
A Geo-Based Fine Granularity Air Quality Prediction Using Machine Learning and Internet-of-Things
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_29
Abstract
As the development of economy and industry, air quality decreases as one of the exchanges of our achievements. Although air pollution has already been considered as a global and critical issue over the past decades, there has not been much innovation on the way people monitor and check the quality. Most of the air quality data today is provided by government or professional sensors set up in cities, which does not provide more detailed status in smaller geo locations with finer granularity, such as specific villages, schools, and shopping malls. In this project, we use machine learning to make a mathematical model which could be used to predict the air quality for small geo locations with accuracy and fine granularity. Through series of experiments and comparisons, the most accuracy mathematical model was found, which had a difference percentage less than 20% with the real data.
