Abstract
The paper presents the adaptation procedure concerning the application of an ANN-based Air Quality forecasting model that was already developed for the city of Gdańsk, Poland, and is now tested for the city of Thessaloniki, Greece. For Gdańsk the model has taken into account the city’s meteorological parameters, which have been implemented using a one-way neural network for ease of learning, as well as the concentration levels of the pollutant of interest, PM10. In the process of teaching the network, four methods of propagation have been used (Back Propagation, Resilient Propagation, Manhattan Propagation, and Scaled Conjugate Gradient) for the purpose of choosing the best method. Results were then compared with real values which define the full network configuration (minimizing the forecast error). The model was then subjected to a process of adaptation for Thessaloniki. The data acquired through the process of adaptation regarding the PM10 levels used for both model training and testing purposes.
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Howes, R., Skea, J., Whelan, B.: Clean and Competitive: Motivating Environmental Performance in Industry (2013)
Guerreiro, C., Foltescu, V., de Leew, F.: Air quality status and trends in Europe. Atmos. Environ. 98, 376–384 (2014)
Orłowski, C., Sarzyński, A.: A model for forecasting pm10 levels with the use of artificial neural networks. In: Information Systems Architecture and Technology—the use of IT Technologies to Support Organizational Management in Risky Environment, Wrocław (2014)
Orłowski, C., Sarzyńska, M., Sarzyński, A.: Prototyp modelu systemu samouczącego do prognozowania stężenia pyłu pm10 w powietrzu atmosferycznym. In: Innowacje w zarządzaniu i inżynierii produkcji, Opole (2014)
Moussiopoulos, N., Vlachokostas, Ch., Tsilingiridis, G., Douros, I., Hourdakis, C., Naneris, C., Sidiropoulos, C.: Air quality status in Greater Thessaloniki Area and the emission reductions needed for attaining the EU air quality legislation. Sci. Total Environ. 407(4), 1268–1285 (2009)
Google Maps. https://www.google.com/maps/ (2016)
Orłowski, C., Kowalczuk, Z.: Modelowanie procesów zarządzania technologiami informatycznymi. Pomorskie Wydawnictwo Naukowo-Techniczne PWNT (2012)
Sitek, T., Orłowski, C.: Ocena technologii informatycznych—Koncepcja wykorzystania systemów inteligentnych, [w:] R. Knosala (red.), Komputerowo zintegrowane zarządzanie, T. 2, Oficyna Wydawnicza Polskiego Towarzystwa Zarządzania Produkcją, Opole, pp. 153–159 (2007)
Sitek, T., Orłowski, C.: Model of Management of Knowledge Bases in the Information Technology Evaluation Environment. In: Grzech, A., Borzemski, L., Świątek, J., Wilimowska, Z. (eds.) Information Systems Architecture and Technology: Models of the Organisation’s Risk Management, Oficyna Wydawnicza Politechniki Wrocławskiej, pp. 221–231, Wrocław (2008)
Kosiński, R.: Sztuczne sieci neuronowe Dynamika nieliniowa i chaos (2005)
Acknowledgment
The research were conducted partly within S/WZ/1/2014 project financed from Ministry of Science and Higher Education funds.
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Orłowski, C., Sarzyński, A., Karatzas, K., Katsifarakis, N., Nazarko, J. (2017). Adaptation of an ANN-Based Air Quality Forecasting Model to a New Application Area. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_41
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DOI: https://doi.org/10.1007/978-3-319-56660-3_41
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