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Authors: Andreas Humpe 1 ; Lars Brehm 2 and Holger Günzel 2

Affiliations: 1 University of Applied Sciences Munich, Schachenmeierstrasse 35, 80636 Munich, Germany ; 2 University of Applied Sciences Munich, Am Stadtpark 20, 81243 Munich, Germany

Keyword(s): Prediction Model, Air Pollution, Adaptive Neuro-fuzzy Inference System, Support Vector Machine, Multiple Linear Regression.

Abstract: As motor vehicle air pollution is a serious health threat, there is a need for air quality forecasting to fulfil policy requirements, and lower traffic induced air pollution. This article compares the performance of multiple linear regressions, adaptive neuro-fuzzy inference systems, and support vector machines in predicting one-hour ahead particulate matter, nitrogen oxides and ozone concentration in the City of Munich between 2014 and 2018. The models are evaluated with different performance measures in-sample and out-of-sample. The results generally support earlier studies on forecasting air pollution and indicate that adaptive neuro-fuzzy inference systems have the highest predictive power in terms of R-square for all pollutants. Furthermore, ozone can be predicted best, whereas nitrogen oxides are the least predictive pollutants. One reason for the different predictability might be rooted in the short lifetime of nitrogen oxides compared to ozone. The results here should be of i nterest to regulators and municipal traffic managements alike who are interested in predicting air pollution and improve urban air quality. (More)

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Paper citation in several formats:
Humpe, A.; Brehm, L. and Günzel, H. (2021). Forecasting Air Pollution in Munich: A Comparison of MLR, ANFIS, and SVM. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8; ISSN 2184-433X, pages 500-506. DOI: 10.5220/0010184905000506

@conference{icaart21,
author={Andreas Humpe. and Lars Brehm. and Holger Günzel.},
title={Forecasting Air Pollution in Munich: A Comparison of MLR, ANFIS, and SVM},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={500-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010184905000506},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Forecasting Air Pollution in Munich: A Comparison of MLR, ANFIS, and SVM
SN - 978-989-758-484-8
IS - 2184-433X
AU - Humpe, A.
AU - Brehm, L.
AU - Günzel, H.
PY - 2021
SP - 500
EP - 506
DO - 10.5220/0010184905000506

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