Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model

DOI: 10.4236/am.2011.21003   PDF   HTML     5,163 Downloads   10,128 Views   Citations


Risk early-warning of natural disasters is a very intricate non-deterministic prediction, and it was difficult to resolve the conflicts and incompatibility of the risk structure. Risk early-warning factors of natural disasters were differentiated into essential attributes and external characters, and its workflow mode was established on risk early-warning structure with integrated Entropy and DEA model, whose steps were put forward. On the basis of standard risk early-warning DEA model of natural disasters, weight coefficient of risk early-warning factors was determined with Information Entropy method, which improved standard risk early-warning DEA model with non-Archimedean infinitesimal, and established risk early-warning preference DEA model based on integrated entropy weight and DEA Model. Finally, model was applied into landslide risk early-warning case in earthquake-damaged emergency process on slope engineering, which exemplified the outcome could reflect more risk information than the method of standard DEA model, and reflected the rationality, feasibility, and impersonality, revealing its better ability on comprehensive safety and structure risk.

Share and Cite:

F. Wang, Y. Cao and M. Liu, "Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model," Applied Mathematics, Vol. 2 No. 1, 2011, pp. 23-32. doi: 10.4236/am.2011.21003.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. Jarraud, “Reducing the Risk of Natural Disasters through Early Warnings,” Bulletin of the American Meteorological Society, Vol. 86, No. 2, 2005, pp. 155-156.
[2] V. Selman, A. L. Selman and J. Selman, “Earthquake/ Quackery: The Science/Art of Predicting Natural Disasters,” Simulation Series, Vol. 15, No. 1, 1985, pp. 113-117.
[3] H. Yang and R. F. Adler, “Towards an Early-Warning System for Global Landslides Triggered by Rainfall and Earthquake,” International Journal of Remote Sensing, Vol. 28, No. 16, 2007, pp. 3713-3719. doi:10.1080/ 01431160701311242
[4] K. Kubo, “Prediction and Its Reliability,” Civil Engineering in Japan, Vol. 26, No. 12, 1987, pp. 1-10.
[5] A. Charnes, W. W. Cooper and E. Rhodes, “Measuring the Efficiency of Decision-Making Units,” European Journal of Operational Research, Vol. 2, No. 6, 1978, pp. 429-444. doi:10.1016/0377-2217(78)90138-8
[6] J. K. Sengupta, “Measuring Dynamic Efficiency under Risk Aversion,” European Journal of Operational Research, Vol. 74, No. 1, 1994, pp. 61-69. doi:10.1016/ 0377-2217(94)90203-8
[7] Z.-X. Ma and H.-L. Ren, “An Evaluation Method Based on Some Sample Units and Its Applying on FSA,” Systems Engineering—Theory & Practice, Vol. 23, No. 2, 2003, pp. 96-100.
[8] Y. Horibe, “Entropy and Correlation,” IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-15, No. 5, 1985, pp. 641-642.
[9] J. K. Sengupta, “Efficiency Measurement in Stochastic Input-Output Systems,” International Journal of Systems Science, Vol. 13, No. 3, 1982, pp. 273-287. doi:10.1080/ 00207728208926348
[10] Y.-M. Wang and Y. Luo, “DEA Efficiency Assessment Using Ideal and Anti-ideal Decision-Making Units,” Applied Mathematics and Computation (New York), Vol. 173, No. 2, 2006, pp. 902-915.
[11] S. Mehrabian, M. R. Alirezaee and G. R. Jahanshahloo, “Complete Efficiency Ranking of Decision-Making Units in Data Envelopment Analysis,” Computational Optimization and Applications, Vol. 14, No. 2, 1999, pp. 261-266. doi:10.1023/A:1008703501682
[12] L. S. Qu, L. M. Li and J. Lee, “Enhanced Diagnostic Certainty Using Information Entropy Theory,” Advanced Engineering Information, Vol. 17, No. 3-4, 2003, pp. 141-150. doi:10.1016/j.aei.2004.08.002
[13] J. J. Buckley, “Entropy Principles in Decision-Making under Risk,” Risk Analysis, Vol. 5, No. 4, 1985, pp. 303-313. doi:10.1111/j.1539-6924.1985.tb00186.x
[14] S. Bilgi, C. Ipbuker, D. Ucar and M. Sahin, “Map Entropy Analysis of Topographic Data Used in Disaster Information Systems,” Journal of Earthquake Engineering, Vol. 12, No. S2, 2008, pp. 23-36.
[15] X. L. Tan, W. Y. Xu and G. L. Liang, “Application of Extenics Method to Comprehensive Safety Evaluation of Rock Slope,” Chinese Journal of Rock Mechanics and Engineering, Vol. 28, No. 12, 2009, pp. 2503-2509.
[16] Y. P. Yin, F. W. Wang and P. Sun, “Landslide Hazards Triggered by the 2008 Wenchuan Earthquake, Sichuan, China,” Landslides, Vol. 6, No. 2, 2009, pp. 139-151. doi:10.1007/s10346-009-0148-5
[17] G. Q. Chen, “Practical Techniques for Risk Analysis of Earthquake-Induced Landslide,” Chinese Journal of Rock Mechanics and Engineering, Vol. 27, No. 12, 2008, pp. 2395-2402.
[18] C.-T. Lee, C.-C. Huang, J.-F. Lee, K.-L. Pan, M.-L. Lin and J.-J. Dong, “Statistical Approach to Earthquake-Induced Landslide Susceptibility,” Engineering Geology, Vol. 100, No. 1-2, 2008, pp. 43-58. doi:10.1016/j.enggeo. 2008.03.004

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.