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Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
Keywords:
hybrid metaheuristics, differential evolution, evolutionary algorithms, fire prediction, uncertainty reductionAbstract
Fire behavior prediction can be a fundamental tool to reduce losses and damages in emergency situations. However, this process is often complex and affected by the existence of uncertainty. For this reason, from different areas of science, several methods and systems are developed and refined to reduce the effects of uncertainty In this paper we present the Hybrid Evolutionary-Statistical System with Island Model (HESS-IM). It is a hybrid uncertainty reduction method applied to forest fire spread prediction that combines the advantages of two evolutionary population metaheuristics: Evolutionary Algorithms and Differential Evolution. We evaluate the HESS-IM with three controlled fires scenarios, and we obtained favorable results compared to the previous methods in the literature.
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References
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[13] X. Yu and M. Gen, Introduction to Evolutionary Algorithms, vol. 0 of Decision Engineering. London: Springer London, 2010.
[14] R. Storn and K. Price, “Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces,” tech. rep., TR- 95-012, ICSI, Berkeley, CA, 1995.
[15] R. Rothermel, A mathematical model for predicting fire spread in wildland fuels, vol. II. Res. Pap. INT-115, US Dept. of Agric., Forest Service, Intermountain Forest and Range Experiment Station (Ogden, UT.), 1972.
[16] Collin D. Bevins, “fireLib User Manual and Technical Reference,” tech. rep., Intermountain Research Station, Forest Service, U.S. Department of Agriculture., 1996.
[2] S. Sitharama Iyengar and B. R.R., Distributed Sensor Networks: Sensor Networking and Applications, vol. Second Edition. Chapman and Hall/CRC, Florida, 2013.
[3] R. Buyya, High Performance Cluster Computing: Architectures and Systems. Prentice Hall, PTR, NJ, USA, 1999.
[4] M. Méndez-Garabetti, G. Bianchini, M. Tardivo, and P. Caymes-Scutari, “Comparative analysis of performance and quality of prediction between ESS and ESS-IM,” Electronic Notes in Theoretical Computer Science, vol. 314, pp. 45–60, 2015.
[5] M. Méndez-Garabetti, G. Bianchini, P. Caymes-Scutari, and M. Tardivo, “Increase in the quality of the prediction of a computational wildfire behavior method through the improvement of the internal metaheuristic,” Fire Safety Journal, vol. 82, pp. 49–62, 2016.
[6] D. Montgomery and G. Runger, Applied Statistics and Probability for Engineers, vol. 6th Edition. Limusa Wiley & Sons, New Jersey, 2014.
[7] E. Alba and M. Tomassini, “Parallelism and evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 5, pp. 443–462, 2002.
[8] S. Das, S. Mullick, and P. Suganthan, “Recent advances in differential evolution âĂŞ an updated survey,” Swarm and Evolutionary Computation, vol. 27, pp. 1–30, 2016.
[9] M. Méndez-Garabetti, G. Bianchini, P. Caymes-Scutari, and M. L. Tardivo, “Método hı́brido de reducción de incertidumbre aplicado a la predicción del comportamiento de incendios forestales,” in Libro de Actas XXII CACIC 2016, XXII Congreso Argentino de Ciencias de la Computación, (San Luis, Argentina), pp. 159–168, Nueva Editorial Universitaria, 2016.
[10] F. Glover, “Future paths for integer programming and artificial intelligence,” Computers & Operations Research, vol. 13, no. 5, pp. 533–549, 1986.
[11] C. Blum, A. Roli, and M. Sampels, Hybrid metaheuristics–an emerging approach to optimization. Berlin Heidelberg: Springer-Verlag Berlin Heidelberg, 1 ed., 2008.
[12] C. Darwin, On the origins of species by means of natural selection. 1859.
[13] X. Yu and M. Gen, Introduction to Evolutionary Algorithms, vol. 0 of Decision Engineering. London: Springer London, 2010.
[14] R. Storn and K. Price, “Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces,” tech. rep., TR- 95-012, ICSI, Berkeley, CA, 1995.
[15] R. Rothermel, A mathematical model for predicting fire spread in wildland fuels, vol. II. Res. Pap. INT-115, US Dept. of Agric., Forest Service, Intermountain Forest and Range Experiment Station (Ogden, UT.), 1972.
[16] Collin D. Bevins, “fireLib User Manual and Technical Reference,” tech. rep., Intermountain Research Station, Forest Service, U.S. Department of Agriculture., 1996.
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Published
2017-04-01
How to Cite
Méndez Garabetti, M., BIanchini, G., Tardivo, M. L., Caymes Scutari, P., & Gil Costa, G. V. (2017). Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction. Journal of Computer Science and Technology, 17(01), p. 12–19. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/455
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