Simple Versus Composed Temporal Lag Regression with Feature Selection, with an Application to Air Quality Modeling
Estrella Lucena-Sánchez , Fernando Jimenez , Guido Sciavicco , Joanna Kamińska
AbstractAnthropogenic environmental pollution is a known and indisputable issue, and the need of ever more precise and reliable land use regression models is undeniable. In this paper we consider two years of hourly data taken in Wrocław (Poland), that contain the concentrations of NO 2 and NO x in the atmosphere, and, along these, traffic flow, air pressure, humidity, solar duration, temperature, and wind speed. In the quest for an explanation model for the pollution concentrations, we improve and generalize the simple temporal lag regression model, and introduce a composed temporal regression model that entails a transformation of the data to improve the effectiveness of classical learning algorithms. We show that using the latter we obtain more accurate and better interpretable explanation models than using the former, and also than using the original, non-transformed data.
|Publication size in sheets||0.5|
|Book||Castellano Giovanna, Castiello Ciro, Mencar Corrado (eds.): Proceedings of the 13th IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2020), 2020, Institute of Electrical and Electronics Engineers, ISBN 978-1-7281-4384-2, DOI:10.1109/EAIS48028.2020|
|Keywords in English||temporal lag regression; land use regression model|
|Score||= 20.0, 20-04-2021, ChapterFromConference|
|Publication indicators||= 1|
|Citation count*||1 (2021-05-07)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.