The ANN model exhibited an R–squared value of 0.85
📌 3 Key Takeaways
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Objective & Hypothesis:An understanding of pollution sources and emissions, and their interactions with terrain and the atmosphere is the most important step in developing appropriate air pollution management plans and action strategies.
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Key Findings & Data:In this study, relationship between sulphur dioxide (SO 2 ) concentration and meteorological parameters such as wind direction, wind speed, temperature, air pressure, precipitation, sunshine amount, sunshine duration, and relative humidity is modeled by using winter season data.
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Conclusion & Impact:The ANN model exhibited an R–squared value of 0.85
An understanding of pollution sources and emissions, and their interactions with terrain and the atmosphere is the most important step in developing appropriate air pollution management plans and action strategies. In this study, relationship between sulphur dioxide (SO 2 ) concentration and meteorological parameters such as wind direction, wind speed, temperature, air pressure, precipitation, sunshine amount, sunshine duration, and relative humidity is modeled by using winter season data. Since the relation between SO 2 concentrations and many meteorological parameters is complex, an artificial neural network (ANN) model is developed to predict the SO 2 levels. Wind direction is modeled as the combination of two variables, which enables to appropriately define the wind direction. The ANN model exhibited an R–squared value of 0.85
Aktan, M., Botsali, A. R. (2017). ESTIMATION OF WINTER SEASON SULPHUR DIOXIDE CONCENTRATIONS WITH AN ARTIFICIAL NEURAL NETWORK MODEL. The Eurasia Proceedings of Science Technology Engineering and Mathematics, Issue: 1. https://ajindex.com/articles/1551654
Aktan, M. (2008). Artificial Neural Network Modeling of Winter Season Sulphur Dioxide Concentrations in Erzurum City. Fresenius Environmental Bulletin. 17(2), 218-223.
Bridgman, H.A., Davies, T.D., Jickells, T., Hunova, I., Tovey, K., Bridges, K. and Surapipith, V. (2002). Air Pollution in the Krusne Hory region, Czech Republic during the 1990s. Atmospheric Environment. 36, 3375-3389.
Chao, Z. (1990). Urban climate and air pollution in Shangai. Energy Build. 15, 647-656.
Cuhadaroglu, B. and Demirci, E. (1997). Influence of some meteorological factors on air pollution in Trabzon city. Energy Build. 25, 179-184.
Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks. 2, 359–366.
Szepesi, D.J. (1989). Compendium of Regulatory Air Quality Simulation Models. Akademiai Kiado, Budapest. Tirabassi, T., Fortezza, F. and Vandini, W. (1991). Wind circulation and air pollutant concentration in the coastal city of Ravenna. Energy Build. 16, 699-704.
Yildirim, Y. and Bayramoglu M. (2006). Adaptive neurofuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak. Chemosphere. 63, 1575-1582.