Araştırma Makalesi
Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ
Öz
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.
Anahtar Kelimeler
NOx prediction GMDH self-organizing deep learning artificial intelligence air pollutant NOx
Destekleyen Kurum
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Proje Numarası
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Teşekkür
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Kaynakça
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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
- 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
Yıl 2022,
Cilt: 5 Sayı: 1, 56 - 71, 31.03.2022
Öz
Proje Numarası
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Kaynakça
- 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
Toplam 31 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Çevre Bilimleri |
| Bölüm | Research Articles |
| Yazarlar | |
| Proje Numarası | - |
| Yayımlanma Tarihi | 31 Mart 2022 |
| Gönderilme Tarihi | 25 Eylül 2021 |
| Kabul Tarihi | 24 Ocak 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 5 Sayı: 1 |
