Issue 11, 2023

Machine learning for hours-ahead forecasts of urban air concentrations of oxides of nitrogen from univariate data exploiting trend attributes

Abstract

The extraction of multiple attributes from past hours in univariate trends of hourly oxides of nitrogen (NOx) recorded at ground-level sites substantially improves NOx hourly forecasts for at least four hours ahead without assistance from exogenous-variable inputs. The method proposed is evaluated with public datasets of hourly NOx data, compiled from 2017 to 2021, for local sites from multiple cities in central England. The datasets for each urban or roadside site considered include more than 40 000 NOx hourly recordings. The period covered straddles the COVID-19-related lockdowns of 2020, associated with lower vehicle emissions that impacted NOx trends at all the studied sites extending into 2021. Fifteen trend attributes are extracted from the recorded NOx trends relating to the previous twelve hours of recorded data. The attributes considered are easily calculated and include seasonal components, recent-past-hour NOx values, averages of several past hours, and differences and rates of change between selected past hours. A multi-linear regression (MLR) and three machine-learning (ML) models are trained and cross-validated for various yearly intervals within the 2017 to 2021 period. The trained models are then applied to predict up to four hours ahead for 2020 and 2021 as separate testing subsets. The models substantially outperform autoregressive and moving average (MA) methods in their hours-ahead forecasts. Feature importance analysis extracted from the MLR and ML models reveals the flexibility with which the models can give more weight to certain trend attributes depending upon the t + x hour being predicted.

Graphical abstract: Machine learning for hours-ahead forecasts of urban air concentrations of oxides of nitrogen from univariate data exploiting trend attributes

Article information

Article type
Paper
Submitted
14 Janv. 2023
Accepted
01 Sept. 2023
First published
12 Sept. 2023
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Adv., 2023,2, 1505-1526

Machine learning for hours-ahead forecasts of urban air concentrations of oxides of nitrogen from univariate data exploiting trend attributes

D. A. Wood, Environ. Sci.: Adv., 2023, 2, 1505 DOI: 10.1039/D3VA00010A

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements