PuSH - Publikationsserver des Helmholtz Zentrums München: Ensemble machine learning, deep learning, and time series forecasting: Improving prediction accuracy for hourly concentrations of ambient air pollutants.

Informationen

Hinweise zu Qualitätskriterien von Zeitschriften

Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016

CC Licencences

Metriken für Publikationen

Navigation

Startseite

English

EVA: Veröffentlichen

Neuer EVA Antrag

Recherche

Erweiterte Suche

Durchblättern nach ...

... HMGU-Autoren/Konsortien

... Organisationsstruktur

... Zeitschriften

... Publikationstypen

... Forschungsdaten

... Arbeitsgruppen

... Erscheinungsjahr

Publikationen im Überblick

Statistik

HGF Fortschrittsbericht

OA Publikationen

Eintragen

Neue Publikation eintragen

Neue Publikation holen aus...

...EVA

Fehlende Publikation melden

Highlights

Suche

Hilfe & Kontakt

Ansprechpartner

Hilfe

Datenschutz

Helmholtz Open Science

Bibliometrische Indikatoren

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Petric, V.* ; Hussain, H.* ; Casni, K.* ; Vuckovic, M.* ; Schopper, A.* ; Andrijic, Z.U.* ; Kecorius, S. ; Madueno, L.* ; Kern, R.* ; Lovrić, M.*

Ensemble machine learning, deep learning, and time series forecasting: Improving prediction accuracy for hourly concentrations of ambient air pollutants.

Aerosol Air Qual. Res. 24:230317 (2024)

Verlagsversion

DOI

	Open Access Hybrid

Abstract
Metriken
Zusatzinfos

This study aims to improve the generalisation capabilities of machine learning models for modelling hourly air pollutant concentrations in scenarios where access to high-quality data is limited. A diverse set of techniques was implemented to tackle this challenge, encompassing the utilisation of the prophet, random forest, and three different deep learning architectures: long short-term memory networks, convolutional neural networks, and multilayer perceptrons. A hybrid model of random forest and prophet was also tested. The role of the hybrid model was to combine the forecasting strengths of the Prophet model with the predictive power of the Random Forest model to better capture complex temporal patterns in the data. After testing, the hybrid model demonstrated improved generalization capabilities, achieving statistically significant improvements in R 2 for hourly concentrations of NO (improving by 26%), NO2 (enhancing by 18%), PM10 (with changes ranging from an 8% decline to a 35% improvement), and O3 (showcasing R 2 coefficients ranging from 0.83 to 0.87) at five sites in Graz, Austria. The utilisation of surface atmospheric ERA5-Land datasets within the models as model features showed high feature post hoc importance in the best (hybrid) models per pollutant and site. Furthermore, error analysis was performed to understand better the conditions under which these models might fail. The results showed that despite the expectations for models to fail with an increasing timeframe (the test set) from March 2019 to March 2020, the models were sufficiently stable for long-term prediction and thus can be used to forecast and predict air pollution.

Impact Factor

Scopus SNIP

Web of Science
Times Cited

Scopus
Cited By

Altmetric

2.500

0.000