TY  - JOUR
T1  - NARX Modellerini Kullanarak Hava Kalitesi Tahmin Analizinin Uygulanması
TT  - Application of the Air Quality Forecasting Analysis Using NARX Models
AU  - Fırat, Yelda
PY  - 2020
DA  - April
Y2  - 2020
DO  - 10.17714/gumusfenbil.605649
JF  - Gümüşhane Üniversitesi Fen Bilimleri Dergisi
PB  - Gumushane University
WT  - DergiPark
SN  - 2146-538X
SP  - 442
EP  - 455
VL  - 10
IS  - 2
LA  - en
AB  - Havakalite yönetimi ve tahmini çevre sorunlarında hayati derecede önemli bir roloynamaktadır. Hava kalitesi sorununun insan sağlığı ve yaşam kalitesi iledoğrudan ilişkili olduğu bilinmektedir. Bu sorunu çözmek için, literatürde kullanılanbazı alışılagelmiş metotlar bulunmaktadır. Bu çalışma yeni doğrusal olmayanözbağlanımlı dışsal model yöntemini işlemektedir. Bu yöntemde tüm hava kalitesiparametreleri dört farklı yer bakımından sisteme girilmektedir. Bunlar ÇanakkaleMerkez ve Çan, Lapseki ve Biga ilçeleridir. Oluşturulan bu model, hava kaliteistasyonları için Nitrik oksit (NO), Nitrojen oksit (NO2), Nitrojen oksitler(NOX) ve Ozon (O3) gibi bazı ölçülmeyen çevresel kirleticiparametrelerin 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, NOXve O3 parametrelerini ölçmektedir. Sunulan sayısal yöntem sonuçları,ölçüm sonuçları ve çıkarılan ivme hatası ile doğrulanmaktadır. Bu sayısalsonuçlar Çanakkale Merkez için dikkate alınmaktadır. Elde edilen sonuçlar,öngörülen parametre değerlerinin çok başarılı olduğunu ve hata ivmesinin çokdüşük olduğunu göstermektedir. Öğrenme sürecinin başarısı %90'nın üzerindedir.
KW  - Hava Kalitesi
KW  - Çanakkale
KW  - Hava Tahmini
KW  - NARX
N2  - Air quality management and forecasting play acrucially important role in environmental problems. It is known that airquality problem is directly related to the quality of life and human health. Inorder to solve this problem, there are some conventional forecasting methodsused in the literature. This paper presents a new non-linear autoregressiveexogenous model method. In this method, all air quality parameters are entered intothe system for four different locations. These are Çanakkale Central and thedistricts of Çan, Lapseki and Biga. This created model provides obtaining andextracting of some unmeasured environmental pollutant parameters for other airquality stations such as Nitric oxide (NO),Nitrogen oxide (NO2), Nitrogen oxides (NOX) and Ozone (O3).Within these stations, the Çanakkale Central air quality monitoring stationmeasures only Particulatematter (PM10) and Sulfur dioxide (SO2)parameters while others measure the parameters of PM10, PM2.5,SO2, NO, NO2, NOX and O3. Presentednumerical model results are verified with measurement results and extractedacceleration error. These numerical results are realized for Çanakkale Central.Obtained results show that the forecasted parameter values are very successfuland error acceleration is very low. The success of the learning process is over90%.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - URL-1,https://www.unece.org/environmental-policy/conventions/envlrtapwelcome/cross-sectoral-linkages/air-pollution-and-food-production.html. May 1, 2018.
CR  - URL-2, www.havaizleme.gov.tr. November 2, 2019.
CR  - URL-3, https://www.canakkalekalem.com/biga-zehir-soluyor/. November 8, 2019.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
UR  - https://doi.org/10.17714/gumusfenbil.605649
L1  - https://dergipark.org.tr/en/download/article-file/1043298
ER  -