The Mathematical Model for Typhoon Disaster Management

This chapter introduces the management methodology of modeling typhoon disaster with focuses on describing an ideal mathematical way to represent typhoon risk. The mathematical model is based on the pattern structure to estimate the relationship between different characteristics in a typhoon event. The prediction results are calculated by the predictor from the disaster event. The candidate indexes of each pattern are selected from important factors in the literatures. Based on the approach, the relationship between the environmental events, the ecosystem change, the economic loss, and the response measure can be evaluated. The model can be further improved as long as the database of the predictor becomes sufficient and the mathematical scheme is accurate. The development of fuzzy theory, neural-network, and intelligent system can be helpful for the future development of this system.
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References
- Anh, V.V., Lam, K.C., Leung, Y., Tieng, Q.M.: Multifractal analysis of Hong Kong air quality data. Environmetrics 11(2), 139–149 (2000) ArticleGoogle Scholar
- Bouchaud, J.P., Potters, M.: Theory of Financial Risk. Cambridge University Press, Cambridge (2000) Google Scholar
- Carr, V., Tah, J.: A fuzzy approach to construction project risk assessment and analysis. Adv. Eng. Softw. 32, 847–857 (2001) ArticleGoogle Scholar
- Chang, N.B., Wang, S.F.: The development of an environmental decision support system for municipal solid waste management. Comput. Environ. Urban Syst. 20(3), 201–212 (1996) ArticleGoogle Scholar
- Chang, N.B., Wei, Y.L., Tseng, C.C., Kao, C.Y.: The design of a GIS-based decision support system for chemical emergency preparedness and response in an urban environment. Comput. Environ. Urban Syst. 21(1), 67–94 (1997) ArticleGoogle Scholar
- Chapman, R.: The controlling influences on effective risk identification and assessment for construction design. Int. J. Project Manage. 19(3), 147–160 (2001) ArticleGoogle Scholar
- Chapman, C., Ward, S.: Project Risk Management: Processes, Techniques and Insights, 2nd edn. Wiley, Chichester (2004) Google Scholar
- Chen, W.K.: A framework for nuclear power plant emergency response system. Nucl. Sci. Technol. 21, 375–378 (2010a) Google Scholar
- Chen, W.K., Sui, G.J., Tang, D. L., Cai, D. L., Wang, J.S.: Study of environmental risk management by multivariable analysis of pattern structure. Paper presented at proceeding of the fourth international conference on management science and engineering management, Chungli, Taiwan, (2010a) Google Scholar
- Chen, W.K., Sui, G.J., Tang, D.L.: A fuzzy intelligent decision support system for typhoon disaster management. Paper presented at proceeding of 2011 IEEE international conference on fuzzy systems, institute of electrical and electronics engineers (IEEE), Taipei, Taiwan, (2010b) Google Scholar
- Chen, W.K., Sui, G.J., Tang, D.L.: Predicting the economic loss of typhoon by case base reasoning and fuzzy theory. Paper presented at proceeding of 9th international conference on machine learning and cybernetics (ICMLC), institute of electrical and electronics engineers (IEEE), systems, man, and cybernetics society (IEEE SMC society), Guilin, China, (2011) Google Scholar
- Chen, W.K.: An approach to pattern recognition by fuzzy category and neural network simulation. Paper presented at proceeding of the 8th international conference on machine learning and cybernetics (ICMLC), institute of electrical and electronics engineers (IEEE), systems, man, and cybernetics society (IEEE SMC society), Qingdao, China, (2010b) Google Scholar
- Chen, W.K.: Environmental applications of granular computing and intelligent systems. In: Pedrycz, W., Chen, S. (eds.) MGranular Computing and Intelligent Systems: Design with Information Granules of Higher Order and Higher Type, Reference Library, vol. 13, pp. 275–301. Springer, Berlin, Germany (2011) Google Scholar
- Chen, W.K.: Fuzzy forecasting with fractal analysis for the time series of environmental pollution. In: Pedrycz, W., Chen, S. (eds.) MTime Series Analysis, Modeling and Applications Intelligent Systems, Reference Library, vol. 47, pp. 199–213, Springer, Berlin, Germany (2013) Google Scholar
- Choi, H., Ahn, J., et al.: A framework for managing risks on concurrent engineering basis. Int. J. Manage. Sci. Eng. Manage. 5(1), 44–52 (2010) Google Scholar
- Cooper, L.: A research agenda to reduce risk in new product development through knowledge management: a practitioner perspective. J. Eng. Tech. Manage. 20(1–2), 117–140 (2003) ArticleGoogle Scholar
- Coppendale, J.: Manage risk in product and process development and avoid unpleasant surprises. Eng. Manage. J. 5(1), 35–38 (1995) ArticleGoogle Scholar
- Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001) Google Scholar
- Evertsz, C.J.G.: Fractal geometry of financial time series. Fractals 3, 609–616 (1995) ArticleGoogle Scholar
- Flanagan, R., Norman, G.: Risk Management and Construction. Blackwell Science Pty Ltd, Victoria, Australia (1993) Google Scholar
- Godano, C., Alonzo, M.L., Vilardo, G.: Multifractal approach to time clustering of earthquakes; application to Mt Vesuvio seismicity. Pure Appl. Geophys. 149(2), 375–390 (1997) ArticleGoogle Scholar
- Godano, C., Tosi, P., Derubeis, V., Augliera, P.: Scaling properties of the spatio–temporal distributionof earthquakes: a multifractal approach applied to a Californian catalogue. Geophys. J. Int. 136, 99–108 (1999) ArticleGoogle Scholar
- Hillson, D.: Extending the risk process to manage opportunity. Int. J. Project Manage. 20, 235–240 (2002) ArticleGoogle Scholar
- Ho, D.S., Lee, C.K., Wang, C.C., Chuang, M.: Scaling characteristics in the Taiwan stock market. Phys. A 332, 448–460 (2004) ArticleGoogle Scholar
- Hopfield, J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. USA 79, 2554–2558 (1982). Biophysics ArticleGoogle Scholar
- Katsuragi, H.: Multiaffinity and entropy spectrum of self-affine fractal profiles. Phys. A 278, 275–281 (2000) ArticleGoogle Scholar
- Lee, C.K.: Multifractal characteristics in. Air pollutant concentration time Series. Water Air Soil Pollut. 135(1–4), 389–409 (2002) ArticleGoogle Scholar
- Lee, C.K., Ho, D.S., Yu, C.C., Wang, C.C.: Fractal analysis of temporal variation of air pollutant concentration by box counting. Environ. Model. Softw. 18(3), 243–251 (2003a) ArticleGoogle Scholar
- Lee, C.K., Ho, D.S., Yu, C.C., Wang, C.C., Hsiao, Y.H.: Simple multifractal cascade model for air pollutant concentration (APC) time series. Environmetrics 14(2), 255–269 (2003b) ArticleGoogle Scholar
- Lee, C.K., Juang, L.C., Wang, C.C., Liao, Y.Y., Liu, Y.C., Ho, D.S.: Scaling characteristics in ozone concentration time series (OCTS). Chemosphere 62(6), 934–946 (2006a) ArticleGoogle Scholar
- Lee, C.K., Yu, C.C., Wang, C.C., Hwang, R.D., Yu, G.K.: Scaling characteristics in aftershock sequence of earthquake. Phys. A 371(2), 692–702 (2006b) ArticleGoogle Scholar
- Linstone, H.A., Murray, T. (eds.): The Delphi Method: Techniques and Applications, Addison-Wesley (1975) Google Scholar
- Makuil, A., Mojtahedi, S., Mousavi, S.: Project risk identification and analysis based on group decision making methodology in a fuzzy environment. Int. J. Manage. Sci. Eng. Manage. 5(2), 108–118 (2010) Google Scholar
- Mandelbrot, B.B.: Fractal and Scaling in Finance: Discontinuity, Concentration, Risk. Springer, NY (1997) BookGoogle Scholar
- Mantegna, R., Stanley, N.H.E.: An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge (2000) Google Scholar
- Mojtahedi, S., Mousavi, S., et al.: A nonparametric statistical approach for analyzing risk factor data in risk management process. J. Appl. Sci. 9, 113–120 (2009) ArticleGoogle Scholar
- Novák, V., Perfilieva, I., Močkoř, J.: Mathematical Principles of Fuzzy Logic Dodrecht. Kluwer Academic, Boston (1999) BookGoogle Scholar
- Olsson, J., Niemczynowicz, J., Berndtsson, R., Larson, M.: An analysis of the rainfall time structure by box counting—some practical implication. J. Hydrol. 137, 261–277 (1992) ArticleGoogle Scholar
- Olsson, J., Niemczynowicz, J., Berndtsson, R.: Fractal analysis of high-resolution rainfall time series. J. Geophys. Res. 98, 23265–23274 (1993) ArticleGoogle Scholar
- Patrick, X., Zhang, G., et al.: Understanding the key risks in construction projects in China. Int. J. Project Manage. 25, 601–614 (2007) ArticleGoogle Scholar
- Raftery, J.: Risk Analysis in Project Management. E&FN Spon, London (1999) Google Scholar
- Saaty, T.L., The analytic hierarchy process, McGraw-Hill, New York, (1980) Google Scholar
- Savic, S., Kayis, B.: Knowledge elicitation for risk mapping in concurrent engineering projects. Int. J. Prod. Res. 44(9), 1739–1755 (2006) ArticleGoogle Scholar
- Schuermann, J.: Pattern Recognition. Wiley, New York (1996) Google Scholar
- Skjeltorp, J.A.: Scaling in the Norwegian stock market. Phys. A 283, 486–528 (2000) ArticleGoogle Scholar
- Telesca, L., Lapenna, V., Macchiato, M.: Mono and multi-fractal investigation of scaling properties in temporal pattern of seismic sequences. Chaos Soliton Fractals 19, 1–15 (2004) ArticleGoogle Scholar
- Thevendran, V., Mawdesley, M.: Perception of human risk factors in construction projects: an exploratory study. Int. J. Project Manage. 22, 131–137 (2004) ArticleGoogle Scholar
- Turiel, A., Pérez-Vicente, C.J.: Multifractal geometry. Phys. A 322, 629–649 (2003) ArticleGoogle Scholar
- Wang, Y., Elhag, T.: Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Syst. Appl. 31, 309–319 (2006) ArticleGoogle Scholar
- Yeung, M.R., Ching, E.M.: RADIS—a regional nuclear accident consequence analysis model for Hong Kong. Nucl. Technol. 101, 123–139 (1993) Google Scholar
- Yoshikawa, T., Kimura, F., Koide, T., Kurita, S.: An emergency computation model for the wind field and diffusion during accident nuclear pollutants release. Atmos. Environ. 24A, 2739–2748 (1990) ArticleGoogle Scholar
- Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965) ArticleGoogle Scholar
Acknowledgments
This study was supported by the following research projects awarded to DL Tang: (1) National Natural Sciences Foundation of China (31061160190, 40976091, NSFC-RFBR Project-41211120181 of DL Tang and D. Pozdnyakov); (2) Guangdong Sciences Foundation (2010B031900041, 8351030101000002); (3) Innovation Group Program of State Key Laboratory of Tropical Oceanography (LTOZZ1201).
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Authors and Affiliations
- Department of Environmental and Property Management, Jinwen University of Science and Technology, New Taipei, 23154, Taiwan, China Wang-Kun Chen
- GuangDong University of Foreign Studies, Guangzhou, China Guang-Jun Sui
- South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China DanLing Tang
- Wang-Kun Chen