Концептуальне моделювання спалахів лісових пожеж на основі онтологічного підходу datamining. Частина 1

M. Radovanović, Y. I. Vyklyuk, M. Milenković, A. Jovanović, D. Vuković, M. Stevančević, N. O. Matsiuk, T. B. Leko

Анотація


Протестовано геліоцентричну гіпотезу причин спалахів лісових пожеж. Знайдено докази кореляції між раптовим надходженням заряджених частинок з боку сонця і виникненням лісових пожеж із затримкою від одного до чотирьох днів. Проведено порівняльний аналіз методів ANFIS та нейронних мереж у задачі пошуку функціональної залежності між виникненням лісових пожеж і факторами, що характеризують сонячну активність. Використано декілька методів аналізу (методи усунення сезонності, R/S-аналіз, DataMining) для встановлення потенційних зв’язків між коливаннями певних параметрів, що характеризують сонячну активність, і виникненням лісових пожеж з урахуванням затримки в часі. Знайдено наявність взаємозв’язку і розроблено прогностичний сценарій, який засновано на ANFIS та ней-ромережевих технологіях. Ці методи, в деяких випадках, дозволяють досягнути точності прогнозування до 93%.

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Посилання


Radovanovic M., Gomes J. Solar Activity and Forest Fires. — Nova Science Publishers Inc, 2009. — 109 p.

Nikolov N. Global Forest Resources Assessment 2005 – Report on fires in the Balkan Region. Forestry Department / N. Nikolov, Fire Management Working Papers FM/11/E, Rome, 2006. — 38 p.

Forest Fires in Europe, Middle East and North Africa 2011. [ G. Schmuck, J. San-Miguel-Ayanz, A. Camia, et al.], Publication office of the European union, 2012. — 108 p.

Incident Management Situation Report Archives. — http://www.predictiveservices.nifc.gov/intelligence/archive.htm.

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Hall L.B. Precipitation associated with lightning-ignited wildfires in Arizona and New Mexico // Int. J. Wildland Fire. — 2007. — 16 (2). — P. 242–254.

Cumming S.G. Forest type and wildfire in the Alberta boreal mixedwood: What do fires burn? // Ecol. Appl. — 2001. — 11 (1). — P. 97–110.

Wotton M.B., StockAn J.B., Martell L.D. An index for tracking sheltered forest floor moisture within the Canadian Forest Fire Weather Index Systemton // Int. J. Wildland Fire. — 2005. — 14(2). — P. 169–182.

Sannikov S.N., Zakharov A.I., Smol’nikova L.G., Sannikova N.S. Forest fires caused by lightning as an indicator of connections between atmosphere, lithosphere, and biosphere // Ekologiya. — 2010. — 41(1). — P. 1–6.

Viegas D.X. Forest fire propagation // Phil. Trans. R. Soc. London Ser. A. —1998. — P. 2907–2928.

Guyette P.R., Stambaugh C.M., Dey C.D., Muzika R.M. Predicting Fire Frequency with Chemistry and Climate Ecosystems // Ecosystems. — 2012. — 15(2). — P. 322–335.

Gomes J., Radovanovic M. Solar activity as a possible cause of large forest fires — а case study: Analysis of the Portuguese forest fires // Sci. Total Environ. — 2008. — 394 (1). — P. 197–205.

Incident Management Situation Report (IMSR) Archives. — http://www. predictiveservices.nifc.gov/intelligence/archive.htm.

SWPC Anonymous FTP Server Historical SWP Products from 1996 to 2008. — http://www.swpc.noaa.gov/ftpmenu/warehouse.html.

MTOF/PM Data by Carrington Rotation. — http://umtof.umd.edu/pm/crn/.

Ducic V., Milenkovic M., Radovanovic M. Contemporary Climate Variability and Forest Fires in Deliblatska pescara // Journal of the Geographical institute Jovan Cvijic. — 2008. — 58 (1). — P. 59−74.

Radovanović M. Forest fires in Europe from July 22nd to 25th 2009 // Arch. Biol. Sci. Belgrade. — 2010. — 62 (2). — P. 419−424.

Radovanovic M. Solar Activity – Climate Change and Natural Disasters in Mountain Regions // Sustainable Development in Mountain Regions. Belgrade. — 2011. — P. 9−17.

Boxall M. ESS Guidelines on Seasonal Adjustment. Eurostat. — Luxembourg: Office for Official Publications of the European Communities. — http://epp. eurostat.ec.europa.eu/portal/page/portal/national_accounts/documents/ESS_Guidelines_on_SA.pdf.

Bell W.R. Economic Time Series: Modeling and Seasonality. [W.R. Bell, S.H. Holan, T.S. McElroy] CRC. — 2012. — 554 p.

Hansen B.E. Econometrics. University of Wisconsin, Department of Economics // Debopam Bhattacharya, Oxford. — 2014. — P. 378.

Labitzke K. The global signal of the 11-year sunspot cycle in the atmosphere: When do we need the QBQ? // Meteorolog. Zeitschrift. Berlin. — 2003. — 12(4). — P. 209−216.

Lenskiy A. The analysis of R/S estimation algorithm with applications to WiMAX network traffic // International Journal of Multimedia and Ubiquitous Engineering. — 2012. — 7 (3). — P. 27−34.

Velsquez Valle M.A., Oleschko L., Klaudia Corral, Ruiz J.A., Korvin Gabor. Spatial Variability of the Hurst Exponent for the Daily Scale Rainfall Series in the State of Zacatecas, Mexico // Journal of Applied Meteorology and Climatology. — 2013. — 52(12). — P. 2771−2780.

Ozger M. Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling // Expert Systems with Applications. — 2011. — 38 (5). — P. 6269−6274.

Peters E., Wiley J. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics // John Wiley & Sons, 1994. — 315 p.

Radovanović M., Vyklyuk Y., Jovanović A., Vuković D., Milenković M., Stevančević M. Examination of the correlations between forest fires and solar activity using Hurst index // Journal of the Geographical institute Jovan Cvijic SASA. — 2013. — 63 (3). — P. 23−32.

Amini M., Abbaspour K.C., Johnson C.A. A comparison of different rule-based statistical models for modeling geogenic groundwater contamination // Environmental Modelling Software. — 2010. — 25(12) — P. 1650−1657.

Bektas Ekici B., Teoman Aksoy U. Prediction of building energy needs in early stage of design by using ANFIS // Expert Systems with Applications. — 2011. — 38(5). — P. 5352−5358.

Betul Bektas Ekici, Teoman Aksoy U. An approach based on ANFIS input selection and modeling for supplier selection problem // Expert Systems with Applications. — 2011. — 38(12). — P. 14907−14917.

Kurtulus B., Flipo N. Hydraulic head interpolation using anfis — model selection and sensitivity analysis // Computers & Geosciences. — 2012. — 38(1). — P. 43−51.

Shiri J., Kisi O., Yoon H., Lee K. Predicting groundwater level fluctuations with meteorological effect implications — A comparative study among soft computing techniques // Computers & Geosciences. — 2013. — 56. — P. 32−44.

Soltani F. Developing operating rules for reservoirs considering the water quality issues: Application of ANFIS-based surrogate models / F. Soltani, R. Kerachian, E. Shirangi // Expert Systems with Applications. — 2010. — 37(9). — P. 6639−6645.

Rowell A., Moore F., Peter F. Global Review of Forest Fires.; IUCN.Gland, Swetzerland, 2000. — 64 p.

Mitra S.K. Is Hurst Exponent Value Useful in Forecasting Financial Time Series? // Asian Social Science. — 2012. — 8 (8). — P. 111–120.

Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Inteligence // Prentice Hall, 1997. — 614 p.

Yilmaz I., Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils // Expert Systems with Applications. — 2011. — 38 (5). — P. 5958−5966.


Пристатейна бібліографія ГОСТ


1. Radovanovic M., Gomes J. Solar Activity and Forest Fires. — Nova Science Publishers Inc, 2009. — 109 p.

2. Nikolov N. Global Forest Resources Assessment 2005 – Report on fires in the Balkan Region. Forestry Department / N. Nikolov, Fire Management Working Papers FM/11/E, Rome, 2006. — 38 p.

3. Forest Fires in Europe, Middle East and North Africa 2011. [ G. Schmuck, J. San-Miguel-Ayanz, A. Camia, et al.], Publication office of the European union, 2012. — 108 p.

4. Incident Management Situation Report Archives. — http://www.predictiveservices.nifc.gov/intelligence/archive.htm.

5. Kourtz P.H., Todd J.B. Predicting the daily occurrence of lightning-caused forest fires / Forestry Canada, Petawawa National Forestry Institute, Chalk River, Ontario. — Information Report PI-X-112, 1991. — 26 p.

6. Hall L.B. Precipitation associated with lightning-ignited wildfires in Arizona and New Mexico // Int. J. Wildland Fire. — 2007. — 16 (2). — P. 242–254.

7. Cumming S.G. Forest type and wildfire in the Alberta boreal mixedwood: What do fires burn? // Ecol. Appl. — 2001. — 11 (1). — P. 97–110.

8. Wotton M.B., StockAn J.B., Martell L.D. An index for tracking sheltered forest floor moisture within the Canadian Forest Fire Weather Index Systemton // Int. J. Wildland Fire. — 2005. — 14(2). — P. 169–182.

9. Sannikov S.N., Zakharov A.I., Smol’nikova L.G., Sannikova N.S. Forest fires caused by lightning as an indicator of connections between atmosphere, lithosphere, and biosphere // Ekologiya. — 2010. — 41(1). — P. 1–6.

10. Viegas D.X. Forest fire propagation // Phil. Trans. R. Soc. London Ser. A. —1998. — P. 2907–2928.

11. Guyette P.R., Stambaugh C.M., Dey C.D., Muzika R.M. Predicting Fire Frequency with Chemistry and Climate Ecosystems // Ecosystems. — 2012. — 15(2). — P. 322–335.

12. Gomes J., Radovanovic M. Solar activity as a possible cause of large forest fires — а case study: Analysis of the Portuguese forest fires // Sci. Total Environ. — 2008. — 394 (1). — P. 197–205.

13. Incident Management Situation Report (IMSR) Archives. — http://www. predictiveservices.nifc.gov/intelligence/archive.htm.

14. SWPC Anonymous FTP Server Historical SWP Products from 1996 to 2008. — http://www.swpc.noaa.gov/ftpmenu/warehouse.html.

15. MTOF/PM Data by Carrington Rotation. — http://umtof.umd.edu/pm/crn/.

16. Ducic V., Milenkovic M., Radovanovic M. Contemporary Climate Variability and Forest Fires in Deliblatska pescara // Journal of the Geographical institute Jovan Cvijic. — 2008. — 58 (1). — P. 59−74.

17. Radovanović M. Forest fires in Europe from July 22nd to 25th 2009 // Arch. Biol. Sci. Belgrade. — 2010. — 62 (2). — P. 419−424.

18. Radovanovic M. Solar Activity – Climate Change and Natural Disasters in Mountain Regions // Sustainable Development in Mountain Regions. Belgrade. — 2011. — P. 9−17.

19. Boxall M. ESS Guidelines on Seasonal Adjustment. Eurostat. — Luxembourg: Office for Official Publications of the European Communities. — http://epp. eurostat.ec.europa.eu/portal/page/portal/national_accounts/documents/ESS_Guidelines_on_SA.pdf.

20. Bell W.R. Economic Time Series: Modeling and Seasonality. [W.R. Bell, S.H. Holan, T.S. McElroy] CRC. — 2012. — 554 p.

21. Hansen B.E. Econometrics. University of Wisconsin, Department of Economics // Debopam Bhattacharya, Oxford. — 2014. — P. 378.

22. Labitzke K. The global signal of the 11-year sunspot cycle in the atmosphere: When do we need the QBQ? // Meteorolog. Zeitschrift. Berlin. — 2003. — 12(4). — P. 209−216.

23. Lenskiy A. The analysis of R/S estimation algorithm with applications to WiMAX network traffic // International Journal of Multimedia and Ubiquitous Engineering. — 2012. — 7 (3). — P. 27−34.

24. Velsquez Valle M.A., Oleschko L., Klaudia Corral, Ruiz J.A., Korvin Gabor. Spatial Variability of the Hurst Exponent for the Daily Scale Rainfall Series in the State of Zacatecas, Mexico // Journal of Applied Meteorology and Climatology. — 2013. — 52(12). — P. 2771−2780.

25. Ozger M. Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling // Expert Systems with Applications. — 2011. — 38 (5). — P. 6269−6274.

26. Peters E., Wiley J. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics // John Wiley & Sons, 1994. — 315 p.

27. Radovanović M., Vyklyuk Y., Jovanović A., Vuković D., Milenković M., Stevančević M. Examination of the correlations between forest fires and solar activity using Hurst index // Journal of the Geographical institute Jovan Cvijic SASA. — 2013. — 63 (3). — P. 23−32.

28. Amini M., Abbaspour K.C., Johnson C.A. A comparison of different rule-based statistical models for modeling geogenic groundwater contamination // Environmental Modelling Software. — 2010. — 25(12) — P. 1650−1657.

29. Bektas Ekici B., Teoman Aksoy U. Prediction of building energy needs in early stage of design by using ANFIS // Expert Systems with Applications. — 2011. — 38(5). — P. 5352−5358.

30. Betul Bektas Ekici, Teoman Aksoy U. An approach based on ANFIS input selection and modeling for supplier selection problem // Expert Systems with Applications. — 2011. — 38(12). — P. 14907−14917.

31. Kurtulus B., Flipo N. Hydraulic head interpolation using anfis — model selection and sensitivity analysis // Computers & Geosciences. — 2012. — 38(1). — P. 43−51.

32. Shiri J., Kisi O., Yoon H., Lee K. Predicting groundwater level fluctuations with meteorological effect implications — A comparative study among soft computing techniques // Computers & Geosciences. — 2013. — 56. — P. 32−44.

33. Soltani F. Developing operating rules for reservoirs considering the water quality issues: Application of ANFIS-based surrogate models / F. Soltani, R. Kerachian, E. Shirangi // Expert Systems with Applications. — 2010. — 37(9). — P. 6639−6645.

34. Rowell A., Moore F., Peter F. Global Review of Forest Fires.; IUCN.Gland, Swetzerland, 2000. — 64 p.

35. Mitra S.K. Is Hurst Exponent Value Useful in Forecasting Financial Time Series? // Asian Social Science. — 2012. — 8 (8). — P. 111–120.

36. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Inteligence // Prentice Hall, 1997. — 614 p.

37. Yilmaz I., Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils // Expert Systems with Applications. — 2011. — 38 (5). — P. 5958−5966.