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Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ

Year 2022, Volume: 5 Issue: 1, 56 - 71, 31.03.2022
https://doi.org/10.35208/ert.1000739

Abstract

Air pollution-induced issues involve public health, environmental, agricultural and socio-economic aspects. Therefore, decision-makers need low-cost, efficient tools with high spatiotemporal representation for monitoring air pollutants around urban areas and sensitive regions. Air pollution forecasting models with different time steps and forecast lengths are used as an alternative and support to traditional air quality monitoring stations (AQMS). In recent decades, given their eligibility to reconcile the relationship between parameters of complex systems, artificial neural networks have acquired the utmost importance in the field of air pollution forecasting. In this study, different machine learning regression methods are used to establish a mathematical relationship between air pollutants and meteorological factors from four AQMS (A-D) located between Çerkezköy and Süleymanpaşa, Tekirdağ. The model input variables included air pollutants and meteorological parameters. All developed models were used with the intent to provide instantaneous prediction of the air pollutant parameter NOx within the AQMS and across different stations. In the GMDH (group method of data handling)-type neural network method (namely the self-organizing deep learning approach), a five hidden layer structure consisting of a maximum of five neurons was preferred and, choice of layers and neurons were made in a way to minimize the error. In all models developed, the data were divided into a training (%80) and a testing set (%20). Based on R2, RMSE, and MAE values of all developed models, GMDH provided superior results regarding the NOx prediction within AQMS (reaching 0.94, 10.95, and 6.65, respectively for station A) and between different AQMS. The GMDH model yielded NOx prediction of station B by using station A input variables (without using NOx data as model input) with R2, RMSE and MAE values 0.80, 10.88, 7.31 respectively. The GMDH model is found suitable for being employed to fill in the gaps of air pollution records within and across-AQMS.

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References

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  • Alimissis, A., Philippopoulos, K., Tzanis, C. G., & Deligiorgi, D. (2018). Spatial estimation of urban air pollution with the use of artificial neural network models. Atmospheric environment, 191, 205–213.
  • Asadollahfardi, G., Zangooei, H., & Aria, S. H. (2016). Predicting PM 2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City. Asian Journal of Atmospheric Environment, 10(2), 67–79.
  • Ayturan, Y. A., Ayturan, Z. C., Altun, H. O., Kongoli, C., Tuncez, F. D., Dursun, S., & Ozturk, A. (2020). Short-term prediction of pm2.5 pollution with deep learning methods. Global Nest Journal, 22(1), 126–131. https://doi.org/10.30955/gnj.003208
  • Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019). A review of artificial neural network models for ambient air pollution prediction. Environmental Modelling and Software, 119(June), 285–304. https://doi.org/10.1016/j.envsoft.2019.06.014
  • Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., et al. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment international, 99, 293–302.
  • Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020). A Machine Learning Approach to Predict Air Quality in California. Complexity, 2020(Ml). https://doi.org/10.1155/2020/8049504
  • Chelani, A. B., Rao, C. V. C., Phadke, K. M., & Hasan, M. Z. (2002). Prediction of sulphur dioxide concentration using artificial neural networks. Environmental Modelling & Software, 17(2), 159–166.
  • Cigizoglu, H. K., Alp, K., & Kömürcü, M. (2005). Estimation of Air Pollution Parameters Using Artificial Neural Networks. Advances in Air Pollution Modeling for Environmental Security, (1), 63–75. https://doi.org/10.1007/1-4020-3351-6_7
  • Demirci, E., & Cuhadaroglu, B. (2000). Statistical analysis of wind circulation and air pollution in urban Trabzon. Energy and Buildings, 31(1), 49–53. https://doi.org/10.1016/S0378-7788(99)00002-X
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  • Elminir, H. K. (2005). Dependence of urban air pollutants on meteorology. Science of the Total Environment, 350(1–3), 225–237. https://doi.org/10.1016/j.scitotenv.2005.01.043
  • Farlow, S. J. (2020). Self-organizing methods in modeling: GMDH type algorithms. CrC Press.
  • Kondo, T. (1998). GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem. In Proceedings of the 37th SICE Annual Conference. International Session Papers (pp. 1143–1148). IEEE.
  • Krishan, M., Jha, S., Das, J., Singh, A., Goyal, M. K., & Sekar, C. (2019). Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Quality, Atmosphere & Health, 12(8), 899–908.
  • Kunt, F., Ayturan, Z. C., & Dursun, S. (2016). Used Some Modelling Applications in Air Pollution Estimates. Journal of International Environmental Application and Science, 11(4), 418–425.
  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18–22.
  • Liu, N., Liu, X., Jayaratne, R., & Morawska, L. (2020). A study on extending the use of air quality monitor data via deep learning techniques. Journal of Cleaner Production, 274, 122956. https://doi.org/10.1016/j.jclepro.2020.122956
  • Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F., & Wan, Z. (2019). A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2. 5. Journal of Cleaner Production, 237, 117729.
  • Maleki, H., Sorooshian, A., Goudarzi, G., Baboli, Z., Tahmasebi Birgani, Y., & Rahmati, M. (2019). Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy, 21(6), 1341–1352. https://doi.org/10.1007/s10098-019-01709-w
  • Munir, S., Mayfield, M., Coca, D., Jubb, S. A., & Osammor, O. (2019). Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—a case study in Sheffield. Environmental Monitoring and Assessment, 191(2). https://doi.org/10.1007/s10661-019-7231-8
  • Murtagh, F. (1991). Multilayer perceptrons for classification and regression. Neurocomputing, 2(5–6), 183–197. Oh, S.-K., & Pedrycz, W. (2002). The design of self-organizing polynomial neural networks. Information Sciences, 141(3–4), 237–258.
  • Pak, U., Ma, J., Ryu, U., Ryom, K., Juhyok, U., Pak, K., & Pak, C. (2020). Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Science of the Total Environment, 699, 133561. https://doi.org/10.1016/j.scitotenv.2019.07.367
  • Prasad, K., Gorai, A. K., & Goyal, P. (2016). Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmospheric environment, 128, 246–262.
  • Rafati, L., Ehrampoush, M., Talebi, A., Mokhtari, M., Kheradpisheh, Z., & Dehghan, H. (2014). Modelling the formation of Ozone in the air by using Adaptive Neuro-Fuzzy Inference System (ANFIS)(Case study: city of Yazd, Iran). Desert, 19(2), 131–135.
  • Savic, M., Mihajlovic, I., & Zivkovic, Z. (2013). An anfis–based air quality model for prediction of SO2 concentration in urban area. Serbian Journal of Management, 8(1), 25–38.
  • Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M., & Khorasani, N. (2021). Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air. Scientific Reports, 11(1), 1–9. https://doi.org/10.1038/s41598-021-81455-6
  • Thrace Development Agency. (2019). Thrace Region Plan (Rep.). https://www.trakyaka.org.tr/upload/Node/33264/xfiles/trakya_bolge_%0Aplani_2014-2023.pdf.
  • Vardar, A., Okursoy, R., & Tekin, Y. (2012). Local wind characteristics for east Thrace, Turkey. Energy Sources, Part B: Economics, Planning and Policy, 7(1), 1–9. https://doi.org/10.1080/15567240903226236
  • Varol, G., Tokuç, B., Ozkaya, S., & Çağlayan, Ç. (2021). Air quality and preventable deaths in Tekirdağ, Turkey. Air Quality, Atmosphere and Health, 14(6), 843–853. https://doi.org/10.1007/s11869-021-00983-2
  • Zhang, H., Wang, Y., Hu, J., Ying, Q., & Hu, X. M. (2015). Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environmental Research, 140, 242–254. https://doi.org/10.1016/j.envres.2015.04.004
Year 2022, Volume: 5 Issue: 1, 56 - 71, 31.03.2022
https://doi.org/10.35208/ert.1000739

Abstract

Project Number

-

References

  • Akyürek, Ö., Arslan, O., & Karademir, A. (2013). SO2 Ve PM10 Hava Kirliliği Parametrelerinin CBS ile Konumsal Analizi: Kocaeli Örneği. TMMOB Coğrafi Bilgi Sistemleri Kongresi, 12.
  • Alimissis, A., Philippopoulos, K., Tzanis, C. G., & Deligiorgi, D. (2018). Spatial estimation of urban air pollution with the use of artificial neural network models. Atmospheric environment, 191, 205–213.
  • Asadollahfardi, G., Zangooei, H., & Aria, S. H. (2016). Predicting PM 2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City. Asian Journal of Atmospheric Environment, 10(2), 67–79.
  • Ayturan, Y. A., Ayturan, Z. C., Altun, H. O., Kongoli, C., Tuncez, F. D., Dursun, S., & Ozturk, A. (2020). Short-term prediction of pm2.5 pollution with deep learning methods. Global Nest Journal, 22(1), 126–131. https://doi.org/10.30955/gnj.003208
  • Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019). A review of artificial neural network models for ambient air pollution prediction. Environmental Modelling and Software, 119(June), 285–304. https://doi.org/10.1016/j.envsoft.2019.06.014
  • Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., et al. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment international, 99, 293–302.
  • Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020). A Machine Learning Approach to Predict Air Quality in California. Complexity, 2020(Ml). https://doi.org/10.1155/2020/8049504
  • Chelani, A. B., Rao, C. V. C., Phadke, K. M., & Hasan, M. Z. (2002). Prediction of sulphur dioxide concentration using artificial neural networks. Environmental Modelling & Software, 17(2), 159–166.
  • Cigizoglu, H. K., Alp, K., & Kömürcü, M. (2005). Estimation of Air Pollution Parameters Using Artificial Neural Networks. Advances in Air Pollution Modeling for Environmental Security, (1), 63–75. https://doi.org/10.1007/1-4020-3351-6_7
  • Demirci, E., & Cuhadaroglu, B. (2000). Statistical analysis of wind circulation and air pollution in urban Trabzon. Energy and Buildings, 31(1), 49–53. https://doi.org/10.1016/S0378-7788(99)00002-X
  • Dongol, R. (2015). Evaluation of the usability of low-cost sensors for public air quality information. Master’s Thesis, Department of Informatics Programming and Networks ….
  • Elminir, H. K. (2005). Dependence of urban air pollutants on meteorology. Science of the Total Environment, 350(1–3), 225–237. https://doi.org/10.1016/j.scitotenv.2005.01.043
  • Farlow, S. J. (2020). Self-organizing methods in modeling: GMDH type algorithms. CrC Press.
  • Kondo, T. (1998). GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem. In Proceedings of the 37th SICE Annual Conference. International Session Papers (pp. 1143–1148). IEEE.
  • Krishan, M., Jha, S., Das, J., Singh, A., Goyal, M. K., & Sekar, C. (2019). Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Quality, Atmosphere & Health, 12(8), 899–908.
  • Kunt, F., Ayturan, Z. C., & Dursun, S. (2016). Used Some Modelling Applications in Air Pollution Estimates. Journal of International Environmental Application and Science, 11(4), 418–425.
  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18–22.
  • Liu, N., Liu, X., Jayaratne, R., & Morawska, L. (2020). A study on extending the use of air quality monitor data via deep learning techniques. Journal of Cleaner Production, 274, 122956. https://doi.org/10.1016/j.jclepro.2020.122956
  • Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F., & Wan, Z. (2019). A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2. 5. Journal of Cleaner Production, 237, 117729.
  • Maleki, H., Sorooshian, A., Goudarzi, G., Baboli, Z., Tahmasebi Birgani, Y., & Rahmati, M. (2019). Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy, 21(6), 1341–1352. https://doi.org/10.1007/s10098-019-01709-w
  • Munir, S., Mayfield, M., Coca, D., Jubb, S. A., & Osammor, O. (2019). Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—a case study in Sheffield. Environmental Monitoring and Assessment, 191(2). https://doi.org/10.1007/s10661-019-7231-8
  • Murtagh, F. (1991). Multilayer perceptrons for classification and regression. Neurocomputing, 2(5–6), 183–197. Oh, S.-K., & Pedrycz, W. (2002). The design of self-organizing polynomial neural networks. Information Sciences, 141(3–4), 237–258.
  • Pak, U., Ma, J., Ryu, U., Ryom, K., Juhyok, U., Pak, K., & Pak, C. (2020). Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Science of the Total Environment, 699, 133561. https://doi.org/10.1016/j.scitotenv.2019.07.367
  • Prasad, K., Gorai, A. K., & Goyal, P. (2016). Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmospheric environment, 128, 246–262.
  • Rafati, L., Ehrampoush, M., Talebi, A., Mokhtari, M., Kheradpisheh, Z., & Dehghan, H. (2014). Modelling the formation of Ozone in the air by using Adaptive Neuro-Fuzzy Inference System (ANFIS)(Case study: city of Yazd, Iran). Desert, 19(2), 131–135.
  • Savic, M., Mihajlovic, I., & Zivkovic, Z. (2013). An anfis–based air quality model for prediction of SO2 concentration in urban area. Serbian Journal of Management, 8(1), 25–38.
  • Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M., & Khorasani, N. (2021). Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air. Scientific Reports, 11(1), 1–9. https://doi.org/10.1038/s41598-021-81455-6
  • Thrace Development Agency. (2019). Thrace Region Plan (Rep.). https://www.trakyaka.org.tr/upload/Node/33264/xfiles/trakya_bolge_%0Aplani_2014-2023.pdf.
  • Vardar, A., Okursoy, R., & Tekin, Y. (2012). Local wind characteristics for east Thrace, Turkey. Energy Sources, Part B: Economics, Planning and Policy, 7(1), 1–9. https://doi.org/10.1080/15567240903226236
  • Varol, G., Tokuç, B., Ozkaya, S., & Çağlayan, Ç. (2021). Air quality and preventable deaths in Tekirdağ, Turkey. Air Quality, Atmosphere and Health, 14(6), 843–853. https://doi.org/10.1007/s11869-021-00983-2
  • Zhang, H., Wang, Y., Hu, J., Ying, Q., & Hu, X. M. (2015). Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environmental Research, 140, 242–254. https://doi.org/10.1016/j.envres.2015.04.004
There are 31 citations in total.

Details

Primary Language English
Subjects Environmental Sciences
Journal Section Research Articles
Authors

Can Burak Özkal 0000-0001-9576-2582

Özkan Arslan 0000-0003-1949-3688

Project Number -
Publication Date March 31, 2022
Submission Date September 25, 2021
Acceptance Date January 24, 2022
Published in Issue Year 2022 Volume: 5 Issue: 1

Cite

APA Özkal, C. B., & Arslan, Ö. (2022). Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ. Environmental Research and Technology, 5(1), 56-71. https://doi.org/10.35208/ert.1000739
AMA Özkal CB, Arslan Ö. Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ. ERT. March 2022;5(1):56-71. doi:10.35208/ert.1000739
Chicago Özkal, Can Burak, and Özkan Arslan. “Developing a GMDH-Type Neural Network Model for Spatial Prediction of NOx : A Case Study of Çerkezköy, Tekirdağ”. Environmental Research and Technology 5, no. 1 (March 2022): 56-71. https://doi.org/10.35208/ert.1000739.
EndNote Özkal CB, Arslan Ö (March 1, 2022) Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ. Environmental Research and Technology 5 1 56–71.
IEEE C. B. Özkal and Ö. Arslan, “Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ”, ERT, vol. 5, no. 1, pp. 56–71, 2022, doi: 10.35208/ert.1000739.
ISNAD Özkal, Can Burak - Arslan, Özkan. “Developing a GMDH-Type Neural Network Model for Spatial Prediction of NOx : A Case Study of Çerkezköy, Tekirdağ”. Environmental Research and Technology 5/1 (March 2022), 56-71. https://doi.org/10.35208/ert.1000739.
JAMA Özkal CB, Arslan Ö. Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ. ERT. 2022;5:56–71.
MLA Özkal, Can Burak and Özkan Arslan. “Developing a GMDH-Type Neural Network Model for Spatial Prediction of NOx : A Case Study of Çerkezköy, Tekirdağ”. Environmental Research and Technology, vol. 5, no. 1, 2022, pp. 56-71, doi:10.35208/ert.1000739.
Vancouver Özkal CB, Arslan Ö. Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ. ERT. 2022;5(1):56-71.