AGILE-GISS AGILE: GIScience Series AGILE-GISS AGILE GIScience Ser. 2700-8150 Copernicus Publications Göttingen, Germany 10.5194/agile-giss-3-6-2022 Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide Iskandaryan Ditsuhi https://orcid.org/0000-0001-6668-2700 1 Di Sabatino Silvana https://orcid.org/0000-0003-2716-9247 2 Ramos Francisco 1 Trilles Sergio 1 Institute of New Imaging Technologies (INIT), Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain Department of Physics and Astronomy, University of Bologna, Via Irnerio 46, 40127 Bologna, Italy 10 06 2022 3 6 Copyright: © 2022 Ditsuhi Iskandaryan et al. 2022 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://agile-giss.copernicus.org/articles/3/6/2022/agile-giss-3-6-2022.html The full text article is available as a PDF file from https://agile-giss.copernicus.org/articles/3/6/2022/agile-giss-3-6-2022.pdf

<p>Nitrogen dioxide is one of the most hazardous pollutants identified by the World Health Organisation. Predicting and reducing pollutants is becoming a very urgent task and many methods have been used to predict their concentration, such as physical or machine learning models. In addition to choosing the right model, it is also critical to choose the appropriate features. This work focuses on the spatiotemporal prediction of nitrogen dioxide concentration using Bidirectional Convolutional LSTM integrated with the exploration of nitrogen dioxide and associated features, as well as the implementation of feature selection methods. The Root Mean Square Error and the Mean Absolute Error were used to evaluate the proposed approach.</p><p><a target="_blank" href="https://doi.org/10.17605/OSF.IO/W7VPH"><img cofileid="779365" src="https://contentmanager.copernicus.org/779365/10/locale/ssl" width="150px"></a></p>