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NARX Modellerini Kullanarak Hava Kalitesi Tahmin Analizinin Uygulanması

Yıl 2020, Cilt: 10 Sayı: 2, 442 - 455, 15.04.2020
https://doi.org/10.17714/gumusfenbil.605649

Öz

Hava
kalite yönetimi ve tahmini çevre sorunlarında hayati derecede önemli bir rol
oynamaktadır. Hava kalitesi sorununun insan sağlığı ve yaşam kalitesi ile
doğrudan ilişkili olduğu bilinmektedir. Bu sorunu çözmek için, literatürde kullanılan
bazı alışılagelmiş metotlar bulunmaktadır. Bu çalışma yeni doğrusal olmayan
özbağlanımlı dışsal model yöntemini işlemektedir. Bu yöntemde tüm hava kalitesi
parametreleri dört farklı yer bakımından sisteme girilmektedir. Bunlar Çanakkale
Merkez ve Çan, Lapseki ve Biga ilçeleridir. Oluşturulan bu model, hava kalite
istasyonları için Nitrik oksit (NO), Nitrojen oksit (NO2), Nitrojen oksitler
(NOX) ve Ozon (O3) gibi bazı ölçülmeyen çevresel kirletici
parametrelerin elde edilmesi ve ayıklanmasını sağlamaktadır. Bu istasyonlarda,
Çanakkale Merkez hava kalitesi izleme istasyonu sadece Partikül madde (PM10)
ve Sülfür dioksit (SO2) parametrelerini ölçerken, diğerleri PM10,
PM2.5, SO2, NO, NO2, NOX
ve O3 parametrelerini ölçmektedir. Sunulan sayısal yöntem sonuçları,
ölçüm sonuçları ve çıkarılan ivme hatası ile doğrulanmaktadır. Bu sayısal
sonuçlar Çanakkale Merkez için dikkate alınmaktadır. Elde edilen sonuçlar,
öngörülen parametre değerlerinin çok başarılı olduğunu ve hata ivmesinin çok
düşük olduğunu göstermektedir. Öğrenme sürecinin başarısı %90'nın üzerindedir.

Kaynakça

  • Aryal, R., Kafley, D., Beecham, S. and Morawska, L., 2013. Air Quality in the Sydney Metropolitan Region during the 2013 Blue Mountains Wildfire. Aerosol and Air Quality Research. 18 (9), 2420-2432. doi: 10.4209/aaqr.2017.10.0427.
  • Delavar, M., Gholami, A., Shiran, G., Rashidi, Y., Nakhaeizadeh, G., Fedra, K. and Afshar, S. H., 2019. A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran. International Journal of Geo –Information. 8(2):99, 1-20. doi: 10.3390/ijgi8020099.
  • Fathy El‐Sharkawy, M. and Javed, W., 2018. Study of Indoor Air Quality Level in Various Restaurants in Saudi Arabia. Environment Progress & Sustainable Energy. 37 (5), 1713-1721. doi: 10.1002/ep.12859.
  • Hamaoui-Laguel, L., Meleux, F., Beekmann, M., Bessagnet, B., Génermont, S., Cellier, P. and Létinois, L., 2014. Improving Ammonia Emissions in Air Quality Modelling for France. Atmospheric Environment. 92, 584–595. doi: 10.1016/j.atmosenv.2012.08.002.
  • Hristov, A. N., 2011. Contribution of Ammonia Emitted from Livestock To Atmospheric Fine Particulate Matter (PM 2.5) in the United States. Journal of Dairy Science. 94 (6), 3130–3136. doi: 10.3168/jds.2010-3681.
  • Hung, C-H., Lo, K-C. and Yuan, C-S., 2018. Forming Highly Polluted PMs Caused by the Invasion of Transboundary Air Pollutants: Model Simulation and Discussion. Aerosol and Air Quality Research. 18 (7), 1698-1719. doi: 10.4209/aaqr.2017.11.0488.
  • Khalid, Z., Iqra, A. and Muneeb A., 2017. Decomposing The Linkages Between Energy Consumption, Air Pollution, Climate Change and Natural Resource Depletion in Pakistan. Environmental Progress & Sustainable Energy. 36 (2), 638-648. doi: 10.1002/ep.12519.
  • Kurt, A. and Oktay, A. B., 2010. Forecasting Air Pollutant Indicator Levels with Geographic Models 3 Days in Advance Using Neural Networks. Expert Systems with Applications. 37 (12), 7986-7992. doi: 10.1016/j.eswa.2010.05.093.
  • Jaikumar, R., Nagendra, S. M. S. and Sivanandan, R., 2018. Development of NARX Based Neural Network Model for Predicting Air Quality Near Busy Urban Corridors, In: Zadeh, L. A., Yaser, R.R., Shahbazova, S. N., Reformat, M. Z. and Kreinovich, V. (Eds.), Recent Developments and the New Direction in Soft-Computing Foundations and Applications, Springer International Publishing, 581-593.
  • Kusumaningtyas, S., Aldrian, E., Wati, T. and Atmoko, D., 2018. The Recent State of Ambient Air Quality in Jakarta. Aerosol and Air Quality Research. 18 (4), 2343-2354. doi: 10.4209/aaqr.2017.10.0391.
  • Mickelson, A. and Tsvankin, D., 2017. Water Quality Monitoring for Coupled Food, Energy and Water Systems. Environment Progress & Sustainable Energy. 37 (1), 165-171. doi: doi.org/10.1002/ep.12789.
  • Ortiz-Garcia, E. G., Salcedo-Sanz, S., Perez-Bellido, A. M., Portilla-Figueras, J. A. and Prieto, L., 2010. Prediction of Hourly O3 Concentrations Using Support Vector Regression Algorithms. Atmospheric Environment. 35 (44), 4481-4488. doi: 10.1016/j.atmosenv.2010.07.024.
  • Pisoni, E., Fariha, M., Carnevale, C. and Piroddi, L., 2009. Forecasting Peak Air Pollution Levels Using NARX models. Engineering Applications of Artificial Intelligence. 22(4-5), 593-602. doi: 10.1016/j.engappai.2009.04.002.
  • Sheppard, S. C., Bittman, S. and Bruulsema, T. W., 2010. Monthly Ammonia Emissions from Fertilizers In 12 Çanadian Ecoregions. Çanadian Journal of Soil Science. 90 (1), 113–127. doi: 10.4141/CJSS09006.
  • Sun, F., Yun, D. and Yu, X., 2017. Air Pollution, Food Production and Food Security: A Review from The Perspective of Food System. Journal of Integrative Agriculture. 16 (12): 2945-2962. doi: 10.1016/S2095-3119(17)61814-8.
  • URL-1,https://www.unece.org/environmental-policy/conventions/envlrtapwelcome/cross-sectoral-linkages/air-pollution-and-food-production.html. May 1, 2018.
  • URL-2, www.havaizleme.gov.tr. November 2, 2019.
  • URL-3, https://www.canakkalekalem.com/biga-zehir-soluyor/. November 8, 2019.
  • Wang, D., Wei, S., Luo, H., Yue, C. and Grunder, O., 2017. A Novel Hybrid Model for Air Quality Index Forecasting Based on Two-Phase Decomposition Technique and Modified Extreme Learning Machine. Science of the Total Environment. 580, 719-733. doi: 10.1016/j.scitotenv.2016.12.018.
  • Wilkinson, S., Mills, G., Illidge, R. and Davies, W. J., 2012. How is Ozone Pollution Reducing Our Food Supply? Journal of Experimental Botany. 63(2), 527-536. doi: 10.1093/jxb/err317.
  • Zheng, H., Liu, J., Tang, X., Wang, Z., Wu, H., Yan, P. and Wang, W., 2018. Improvement of the Real-time PM2.5 Forecast over the Beijing-Tianjin-Hebei Region using an Optimal Interpolation Data Assimilation Method. Aerosol and Air Quality Research. 18 (5): 1305–1316. doi: 10.4209/aaqr.2017.11.0522.
  • Zhu, S., Lian, X., Liu, H., Hu, J., Wang, Y. and Che, J., 2017. Daily Air Quality Index Forecasting with Hybrid Models: A Case in China. Environmental Pollution. 231 (Pt 2), 1232-1244. doi: 10.1016/j.envpol.2017.08.069.

Application of the Air Quality Forecasting Analysis Using NARX Models

Yıl 2020, Cilt: 10 Sayı: 2, 442 - 455, 15.04.2020
https://doi.org/10.17714/gumusfenbil.605649

Öz

Air quality management and forecasting play a
crucially important role in environmental problems. It is known that air
quality problem is directly related to the quality of life and human health. In
order to solve this problem, there are some conventional forecasting methods
used in the literature. This paper presents a new non-linear autoregressive
exogenous model method. In this method, all air quality parameters are entered into
the system for four different locations. These are Çanakkale Central and the
districts of Çan, Lapseki and Biga. This created model provides obtaining and
extracting of some unmeasured environmental pollutant parameters for other air
quality stations such as Nitric oxide (NO),
Nitrogen oxide (NO2), Nitrogen oxides (NOX) and Ozone (O3).
Within these stations, the Çanakkale Central air quality monitoring station
measures only
Particulate
matter
(PM10) and Sulfur dioxide (SO2)
parameters while others measure the parameters of PM10, PM2.5,
SO2, NO, NO2, NOX and O3. Presented
numerical model results are verified with measurement results and extracted
acceleration error. These numerical results are realized for Çanakkale Central.
Obtained results show that the forecasted parameter values are very successful
and error acceleration is very low. The success of the learning process is over
90%.

Kaynakça

  • Aryal, R., Kafley, D., Beecham, S. and Morawska, L., 2013. Air Quality in the Sydney Metropolitan Region during the 2013 Blue Mountains Wildfire. Aerosol and Air Quality Research. 18 (9), 2420-2432. doi: 10.4209/aaqr.2017.10.0427.
  • Delavar, M., Gholami, A., Shiran, G., Rashidi, Y., Nakhaeizadeh, G., Fedra, K. and Afshar, S. H., 2019. A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran. International Journal of Geo –Information. 8(2):99, 1-20. doi: 10.3390/ijgi8020099.
  • Fathy El‐Sharkawy, M. and Javed, W., 2018. Study of Indoor Air Quality Level in Various Restaurants in Saudi Arabia. Environment Progress & Sustainable Energy. 37 (5), 1713-1721. doi: 10.1002/ep.12859.
  • Hamaoui-Laguel, L., Meleux, F., Beekmann, M., Bessagnet, B., Génermont, S., Cellier, P. and Létinois, L., 2014. Improving Ammonia Emissions in Air Quality Modelling for France. Atmospheric Environment. 92, 584–595. doi: 10.1016/j.atmosenv.2012.08.002.
  • Hristov, A. N., 2011. Contribution of Ammonia Emitted from Livestock To Atmospheric Fine Particulate Matter (PM 2.5) in the United States. Journal of Dairy Science. 94 (6), 3130–3136. doi: 10.3168/jds.2010-3681.
  • Hung, C-H., Lo, K-C. and Yuan, C-S., 2018. Forming Highly Polluted PMs Caused by the Invasion of Transboundary Air Pollutants: Model Simulation and Discussion. Aerosol and Air Quality Research. 18 (7), 1698-1719. doi: 10.4209/aaqr.2017.11.0488.
  • Khalid, Z., Iqra, A. and Muneeb A., 2017. Decomposing The Linkages Between Energy Consumption, Air Pollution, Climate Change and Natural Resource Depletion in Pakistan. Environmental Progress & Sustainable Energy. 36 (2), 638-648. doi: 10.1002/ep.12519.
  • Kurt, A. and Oktay, A. B., 2010. Forecasting Air Pollutant Indicator Levels with Geographic Models 3 Days in Advance Using Neural Networks. Expert Systems with Applications. 37 (12), 7986-7992. doi: 10.1016/j.eswa.2010.05.093.
  • Jaikumar, R., Nagendra, S. M. S. and Sivanandan, R., 2018. Development of NARX Based Neural Network Model for Predicting Air Quality Near Busy Urban Corridors, In: Zadeh, L. A., Yaser, R.R., Shahbazova, S. N., Reformat, M. Z. and Kreinovich, V. (Eds.), Recent Developments and the New Direction in Soft-Computing Foundations and Applications, Springer International Publishing, 581-593.
  • Kusumaningtyas, S., Aldrian, E., Wati, T. and Atmoko, D., 2018. The Recent State of Ambient Air Quality in Jakarta. Aerosol and Air Quality Research. 18 (4), 2343-2354. doi: 10.4209/aaqr.2017.10.0391.
  • Mickelson, A. and Tsvankin, D., 2017. Water Quality Monitoring for Coupled Food, Energy and Water Systems. Environment Progress & Sustainable Energy. 37 (1), 165-171. doi: doi.org/10.1002/ep.12789.
  • Ortiz-Garcia, E. G., Salcedo-Sanz, S., Perez-Bellido, A. M., Portilla-Figueras, J. A. and Prieto, L., 2010. Prediction of Hourly O3 Concentrations Using Support Vector Regression Algorithms. Atmospheric Environment. 35 (44), 4481-4488. doi: 10.1016/j.atmosenv.2010.07.024.
  • Pisoni, E., Fariha, M., Carnevale, C. and Piroddi, L., 2009. Forecasting Peak Air Pollution Levels Using NARX models. Engineering Applications of Artificial Intelligence. 22(4-5), 593-602. doi: 10.1016/j.engappai.2009.04.002.
  • Sheppard, S. C., Bittman, S. and Bruulsema, T. W., 2010. Monthly Ammonia Emissions from Fertilizers In 12 Çanadian Ecoregions. Çanadian Journal of Soil Science. 90 (1), 113–127. doi: 10.4141/CJSS09006.
  • Sun, F., Yun, D. and Yu, X., 2017. Air Pollution, Food Production and Food Security: A Review from The Perspective of Food System. Journal of Integrative Agriculture. 16 (12): 2945-2962. doi: 10.1016/S2095-3119(17)61814-8.
  • URL-1,https://www.unece.org/environmental-policy/conventions/envlrtapwelcome/cross-sectoral-linkages/air-pollution-and-food-production.html. May 1, 2018.
  • URL-2, www.havaizleme.gov.tr. November 2, 2019.
  • URL-3, https://www.canakkalekalem.com/biga-zehir-soluyor/. November 8, 2019.
  • Wang, D., Wei, S., Luo, H., Yue, C. and Grunder, O., 2017. A Novel Hybrid Model for Air Quality Index Forecasting Based on Two-Phase Decomposition Technique and Modified Extreme Learning Machine. Science of the Total Environment. 580, 719-733. doi: 10.1016/j.scitotenv.2016.12.018.
  • Wilkinson, S., Mills, G., Illidge, R. and Davies, W. J., 2012. How is Ozone Pollution Reducing Our Food Supply? Journal of Experimental Botany. 63(2), 527-536. doi: 10.1093/jxb/err317.
  • Zheng, H., Liu, J., Tang, X., Wang, Z., Wu, H., Yan, P. and Wang, W., 2018. Improvement of the Real-time PM2.5 Forecast over the Beijing-Tianjin-Hebei Region using an Optimal Interpolation Data Assimilation Method. Aerosol and Air Quality Research. 18 (5): 1305–1316. doi: 10.4209/aaqr.2017.11.0522.
  • Zhu, S., Lian, X., Liu, H., Hu, J., Wang, Y. and Che, J., 2017. Daily Air Quality Index Forecasting with Hybrid Models: A Case in China. Environmental Pollution. 231 (Pt 2), 1232-1244. doi: 10.1016/j.envpol.2017.08.069.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Yelda Fırat 0000-0002-6741-2507

Yayımlanma Tarihi 15 Nisan 2020
Gönderilme Tarihi 16 Ağustos 2019
Kabul Tarihi 29 Şubat 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 10 Sayı: 2

Kaynak Göster

APA Fırat, Y. (2020). NARX Modellerini Kullanarak Hava Kalitesi Tahmin Analizinin Uygulanması. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 10(2), 442-455. https://doi.org/10.17714/gumusfenbil.605649