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Air quality data series estimation based on machine learning approaches for urban environments

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Abstract

Air pollution is one of the main environmental problems in residential areas. In many cases, the effects of air pollution on human health can be prevented by forecasting the air quality in the next day. In order to predict the 1 day in advance air quality index (AQI) of Orumiyeh city, the hybrid single decomposition (HSD) and hybrid two-phase decomposition (HTPD) models were used. In the first step, the AQI data were decomposed by complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and was hybridized with general regression neural network (GRNN) and extreme learning machine (ELM) as HSD models. In the second step, using variational mode decomposition (VMD) technique the results of the first intrinsic mode functions (IMFs) of CEEMDAN model were decomposed into nine VMs and were predicted by GRNN and ELM models to obtain IMF1. Finally, in the third step, GRNN and ELM were used again to predict the IMFS as HTPD models. Results showed that in predicting AQI series data by HSD models both CEEMDAN-ELM and CEEMDAN-GRNN models were similarly accurate. Among all the models used, the accuracy of CEEMDAN-VMD-GRNN as the HTPD model was the highest in the training phase (R2 = 0.98, RMSE = 4.13 and MAE = 2.99) and in the testing phase (R2 = 0.74, RMSE = 5.45 and MAE = 3.87). It can be concluded that HTPD models have more accurate results to predict AQI data compared with HSD models.

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Abbreviations

AQI:

air quality index

HSD:

hybrid single decomposition

HTPD:

hybrid two-phase decomposition

CEEMDAN:

complementary ensemble empirical mode decomposition with adaptive noise

GRNN:

general regression neural network

ELM:

extreme learning machine

VMD:

variational mode decomposition

IMFS :

intrinsic mode functions

ARIMA:

autoregressive integrated moving average

MLP:

multilayer perceptron

GLM:

generalized linear models

ANN:

artificial neural networks

MLR:

multiple linear regression

ANFIS:

adaptive neuro-fuzzy inference system

VIF:

variance inflation factor

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Acknowledgments

We thank Dr. Vahid Nimehchisalem, from the Department of English, Faculty of Modern Languages and Communication, Universiti Putra Malaysia, for editing our manuscript. The researchers are grateful to Mrs. Shadi Ausati for her effort of modeling in Matlab software.

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Authors and Affiliations

  1. Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Pasdaran Street, P.O.Box 416, Sanandaj, Iran

    Alireza Rahimpour & Jamil Amanollahi

  2. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran

    Jamil Amanollahi

  3. Climate and Climatic Change Group, Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, University Campus, Bldg Phys-5, 15784, Athens, Greece

    Chris G. Tzanis

Authors
  1. Alireza Rahimpour
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  2. Jamil Amanollahi
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  3. Chris G. Tzanis
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Correspondence to Jamil Amanollahi.

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Rahimpour, A., Amanollahi, J. & Tzanis, C.G. Air quality data series estimation based on machine learning approaches for urban environments. Air Qual Atmos Health 14, 191–201 (2021). https://doi.org/10.1007/s11869-020-00925-4

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  • DOI: https://doi.org/10.1007/s11869-020-00925-4

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