Benzene Prediction: A Comparative Study of ANFIS, LSTM and MLR

Andreas Humpe, Holger Günzel, Lars Brehm

2021

Abstract

It is generally recognized that road traffic emissions are a major health risk and responsible for a substantial share of death and disease in Europe. Although artificial intelligence methods have been used extensively for air pollution forecasting, there is little research on benzene prediction and the use of long short-term memory networks. Benzene is considered one of the pollutants of greatest concern in urban areas and has been linked to leukemia. This paper investigates the predictive power of adaptive neuro-fuzzy inference systems, long short-term memory networks and multiple linear regression models for one hour ahead benzene prediction in the city of Augsburg, Germany. The results of the analysis indicate that adaptive neuro-fuzzy inference systems have the best in sample performance for benzene prediction, whereas long short-term memory networks and multiple linear regressions show similar predictive power. However, long short-term memory models have the best out of sample performance for one hour ahead benzene prediction. This supports the use of long short-term memory networks for benzene prediction in real emission forecasting applications.

Paper Citation

in Harvard Style

Humpe A., Günzel H. and Brehm L. (2021). Benzene Prediction: A Comparative Study of ANFIS, LSTM and MLR. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA; ISBN 978-989-758-534-0, SciTePress, pages 318-325. DOI: 10.5220/0010660900003063

in Bibtex Style

@conference{ncta21,
author={Andreas Humpe and Holger Günzel and Lars Brehm},
title={Benzene Prediction: A Comparative Study of ANFIS, LSTM and MLR},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA},
year={2021},
pages={318-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010660900003063},
isbn={978-989-758-534-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA
TI - Benzene Prediction: A Comparative Study of ANFIS, LSTM and MLR
SN - 978-989-758-534-0
AU - Humpe A.
AU - Günzel H.
AU - Brehm L.
PY - 2021
SP - 318
EP - 325
DO - 10.5220/0010660900003063
PB - SciTePress