Data Driven Forecasting Models for Urban Air Pollution: MoreAir Case Study
Author:
Affiliation:
1. International University of Rabat, Rabat, TICLab, Morocco
2. ENSIAS, Mohammed V University in Rabat, Rabat, Morocco
Funder
Belgium Ministry of Cooperation through the Vlaamse Interuniversitaire Raad-Universitaire Ontwikkelingssamenwerking (VLIR UOS) Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10313279.pdf?arnumber=10313279
Reference33 articles.
1. Spatio-Temporal Forecasting: A Survey of Data-Driven Models Using Exogenous Data
2. MoreAir: A Low-Cost Urban Air Pollution Monitoring System
3. A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy
4. Spatiotemporal predictions using an MSSA approach;awichi,2015
5. An Exploration of Features Impacting Respiratory Diseases in Urban Areas
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