@article {Gryech2020379,
	title = {Machine learning for air quality prediction using meteorological and traffic related features},
	journal = {Journal of Ambient Intelligence and Smart Environments},
	volume = {12},
	number = {5},
	year = {2020},
	note = {cited By 4},
	pages = {379-391},
	abstract = {The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small number of these stations can be deployed in a country. To improve the spatial resolution of the air monitoring process, an interesting idea is to develop data-driven models to predict air quality based on readily available data. In this paper, we investigate the correlations between air pollutants concentrations and meteorological and road traffic data. Using machine learning, regression models are developed to predict pollutants concentration. Both linear and non-linear models are investigated in this paper. It is shown that non-linear models, namely Random Forest (RF) and Support Vector Regression (SVR), better describe the impact of traffic flows and meteorology on the concentrations of pollutants in the atmosphere. It is also shown that more accurate prediction models can be obtained when including some pollutants{\textquoteright} concentration as predictors. This may be used to infer the concentrations of some pollutants using those of other pollutants, thereby reducing the number of air pollution sensors. {\textcopyright} 2020 - IOS Press and the authors. All rights reserved.},
	keywords = {Accurate prediction, Air quality, Air quality monitoring, Air quality prediction, Data-driven model, Decision trees, Forecasting, Machine learning, Non-linear model, Nonlinear systems, Pollution sensors, Predictive analytics, Spatial resolution, Support vector regression, Support vector regression (SVR)},
	doi = {10.3233/AIS-200572},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093821240\&doi=10.3233\%2fAIS-200572\&partnerID=40\&md5=f8b7d726f054f01a2be432560d359fc5},
	author = {Gryech, I. and Ghogho, M. and Elhammouti, H. and Sbihi, N. and Kobbane, A.}
}
@article {Gryech2020,
	title = {Moreair: A low-cost urban air pollution monitoring system},
	journal = {Sensors (Switzerland)},
	volume = {20},
	number = {4},
	year = {2020},
	note = {cited By 25},
	abstract = {MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children{\textquoteright}s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution. {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Agile deployments, Agile manufacturing systems, Air quality, Costs, Decision trees, Geographic information systems, Information systems, Information use, Learning systems, Machine learning, Mobile sensing, Monitoring, Particles (particulate matter), Particulate Matter, Pollution detection, Pollution monitoring, Random forests, Real-time information, Sensor deployment, Urban air pollution, Urban pollutions},
	doi = {10.3390/s20040998},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079609813\&doi=10.3390\%2fs20040998\&partnerID=40\&md5=dc90b4357fcdc64d82d12f57a45f971f},
	author = {Gryech, I. and Ben-Aboud, Y. and Guermah, B. and Sbihi, N. and Ghogho, M. and Kobbane, A.}
}
@article {Rezzouqi20181145,
	title = {Analyzing the accuracy of historical average for urban traffic forecasting using google maps},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {868},
	year = {2018},
	pages = {1145-1156},
	doi = {10.1007/978-3-030-01054-6_79},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057079293\&doi=10.1007\%2f978-3-030-01054-6_79\&partnerID=40\&md5=5c985c898807cd5eb68ed52c62ea6335},
	author = {Rezzouqi, H. and Gryech, I. and Sbihi, N. and Ghogho, M. and Benbrahim, H.}
}
