Air pollution forecasting model control

• Autorzy: Domańska D. , Wojtylak M.
• Języki publikacji: EN

Abstrakty

EN

In the paper we discuss the analysis of multidimensional data. We consider the relationship between them using a special fuzzy number form. Calculations are kept on set of actual and historical meteorological data. Our model using to forecast pollution concentrations is important in today because pollutions have very big influence on our life in particular pollutions PM10 (particulate matter less than 10 µm in diameter). The effects of inhaling particulate matter have been widely studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. Because of the size of the particle, they can penetrate the deepest part of the lungs. In Air Pollution Forecasting Model for the chosen weather forecast we find similar weather forecasts. Next, we find real meteorological situations from the historical data which correspond to them and we create fuzzy numbers, that is, the fuzzy weather forecasts. Then we estimate the validity of the weather forecast on the basis of the historical data and its accuracy. We investigate it with the help of a set of indicators, which corresponds to the parameters of the weather forecast, using the similarities rule of the weather forecast to the meteorological situation, a proper distance and data analysis. This comprehensive analysis allows us to investigate the effectiveness of forecasting pollution concentrations, putting the dependence between particular attributes describing the weather forecast in order and proving the legitimacy of the applicable fuzzy numbers in air pollution forecasting. Models are created for data, which are measured and forecasting in Poland. By reason of this data our models are testing in real sets of data and effects are received in active system.

Słowa kluczowe

EN

fuzzy system models; fuzzy numbers; fuzzy matrix; fuzzy weather forecast; air pollution; forecasting

PL

modele systemu rozmytego; liczby rozmyte; macierz rozmyta; rozmyta prognoza pogody; zanieczyszczenie powietrza; prognozowanie

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 14
• Strony: 9--22
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Domańska D.

• University of Silesia, Institute of Informatics, ul. Będzińska 39, 41-200 Sosnowiec, Poland

autor

Wojtylak M.

Bibliografia

• [1] Weather Forecasting Through the Ages, NASA Facts, http://aqua.nasa.gov, 2002.
• [2] RIGATOSA G., ZHANGB Q., Fuzzy model validation using the local statistical approach, Fuzzy Sets and Systems 160(7) (2009) 882-904.
• [3] LEE C., LIU A., CHEN W., Pattern Discovery of Fuzzy Time Series for Financial Prediction, IEEE Trans. Knowl. Data Eng 18(5) (2006) 613-625.
• [4] KUNHUANG H., Heuristic Models of Fuzzy Time Series for Forecasting, Fuzzy Sets and Systems 123(3) (2001) 369-386.
• [5] JIANG W., WANG P., Research on Interval Prediction of Nonlinear Chaotic Time Series Based on New Neural Networks, in: Proc. 6th World Congress Intell. Control and Automation, 2006, pp. 2835-2839.
• [6] AJIT KUMAR GAUTAM, CHELANI A.B., JAIN V.K., DEVOTTA S., A new scheme to predict chaotic time series of air pollutant concentrations using artificial neural network and nearest neighbor searching, Atmospheric Environ. 42(18) (2008) 4409-4417.
• [7] AZNARTE M.J.L., MANUEL BENÍTEZ SÁNCHEZ J., NIETO LUGILDE D., DE LINARES FERNÁNDEZ C, DÍAZ DE LA GUARDIA C., ALBA SÁNCHEZ F., Forecasting Airborne Pollen Concentration Time Series with Neural and Neuro-Fuzzy Models, Expert Systems with Applications 32(4) (2007) 1218-1225.
• [8] OŚRÓDKA L., KRAJNY E., WOJTYLAK M., The use of numerical weather forecast for air pollution forecasting in an urban industrial agglomeration, in: Proc. 4th Annual Meeting EMS and 5th EC on Applied Climatology, 2004, EMS Vol. 1.
• [9] OŚRÓDKA L., WOJTYLAK M., KRAJNY E., RORBEK K., Improving the methods of forecasting the high concentrations of pollutants in urban-industrial agglomerations, using the numerical weather forecast models, Wiad. MGW 27(1) (2004) 105-116.
• [10] ZADEH L.A., Fuzzy Sets, Inf. and Control 8 (1965) 338-353.
• [11] KLIR G., FOLGER T., Fuzzy Sets, Uncertainty and Information, Prentice Hall PTR, Englewood Cliffs, NJ, 1988.
• [12] ZIMMERMAN H.J., Fuzzy Set Theory and its Applications, second ed., Kluwer, Dordrecht, 1991.
• [13] SILVERT W., Ecological impact classification with fuzzy sets, Ecol. Model. 96 (1997) 1-10.
• [14] HANSEN B.K., RIORDAN D., Weather Prediction Using Case-Based Reasoning and Fuzzy Set Theory, in: Proc. Workshop on Soft Computing in Case-Based Reasoning, International Conf. on Case-Based Reasoning, Canada, 2001, pp.175-178.
• [15] KOWALSKA M., HUBICKI L., ZEJDA E.J., OŚRÓDKA L., KRAJNY E., WOJTYLAK M., Effect of ambient air pollution on daily mortality in Katowice Conurbation, Poland, Polish J. Environ. Stud. (2007) 227-232.
• [16] KOWALSKA M., ZEJDA J.E., OŚRÓDKA L., KLEJNOWSKI K., KRAJNY E., WOJTYLAK M., Relationship between ambient air pollution and daily mortality in the Urban Area of Katowice-comparison on two periods 1994-1995 and 2001-2002, in: Proc. Public Conf. ”Particles and Health-State of the Research and Policy Implications”, (9), 2007.
• [17] DOMAŃSKA D., WOJTYLAK M., Development data serving to forecasting pollution concentrations, 2009, pp. 351-359.
• [18] DOMAŃSKA D., WOJTYLAK M., Selection criteria of forecast pollution concentrations using collateral informations, Computer Methods and Systems, Kraków, 2009, pp.213-218.
• [19] BEYER K.S., GOLDSTEIN J., RAMAKRISHNAN R., SHAFT U., When is “Nearest Neighbour” meaningful?, in: Proc. of the 7th International Conf. on Database Theory, 1999, pp. 217-235.
• [20] OŚRÓDKA L., WOJTYLAK M., KRAJNY E., DUNAL R., KLEJNOWSKI K., Application Data Mining for forecasting of high-level air pollution in urban-industrial area in southern Poland, in: Proc. of the 10th Int. Conf. on Harmonisation within Atmospheric Dispers. Modelling for Regulatory Purposes, 2005, pp. 664-668.
• [21] CHEN L., CHEN G., Fuzzy Modeling, Prediction, and Control of Uncertain Chaotic Systems Based on Time Series, IEEE Trans. Circuits and Systems I, vol. 47, no. 10 (2000).
• [22] BRUNET G., The first hundred years of numerical weather prediction, Proceedings of the 19th Int. Symposium on High Performance Computing Systems and Applications, Canada (2005).
• [23] BANACOS P.C., SCHULTZ D.M., The use of moisture flux convergence in forecasting convective initiation: historical and operational perspectives, Weather and Forecasting, 20, 351-366 (2005).
• [24] STAWIANY W., CABAN P., CIMANDER B., CISOWSKA E., HOŁDA I., KRAJNY E., KRUCZAŁA, OSTROWSKA E., OŚRÓDKA L., RORBEK K., SOCHA S., ŚWIĘCH-SKIBA J., WOJTYLAK M., Monograph of automatic measurements of air quality in Katowice Conurbation (1993-1999), Biuletyn Regionalnego Monitoringu Środowiska: Wojewoda Śląski (2000) 1-324.