Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 9828676, 10 pages
http://dx.doi.org/10.1155/2016/9828676
Research Article

Research and Application of Fire Forecasting Model for Electric Transmission Lines Incorporating Meteorological Data and Human Activities

Jiazheng Lu,1,2 Jun Guo,1,2 Li Yang,1,2 Tao Feng,1,2 and Jie Zhang1,2

1State Key Laboratory of Disaster Prevention and Reduction for Power Grid Transmission and Distribution Equipment, Changsha 410129, China
2State Grid Hunan Electric Power Company Disaster Prevention and Reduction Center, Changsha 410129, China

Received 12 March 2016; Accepted 29 September 2016

Academic Editor: Marco Mussetta

Copyright © 2016 Jiazheng Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. E. Burgan, “Use of remotely sensed date for fire danger estimation,” Remote Sensing of Environment, vol. 4, pp. 1–8, 1996. View at Google Scholar
  2. R. E. Burgan, R. W. Klaver, and J. M. Klarer, “Fuel models and fire potential from satellite and surface observations,” International Journal of Wildland Fire, vol. 8, no. 3, pp. 159–170, 1998. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Vega-Garcia, B. Lee, P. Woodard, and S. Titus, “Applying neural network technology to human-caused wildfire occurence prediction,” AI Applications, vol. 10, pp. 9–18, 1996. View at Google Scholar
  4. G. Wen, X. Hou, and H. Chen, “The application of artifical nervous net to forecasting forest fire,” Journal of Biomathematics, vol. 16, pp. 225–228, 2001. View at Google Scholar
  5. L. Hu, Z. Feng, and Y. Nie, “Forest fire prediction research based on VLBP neural network,” Scientia Silvae Sinicae, vol. 42, pp. 155–158, 2006. View at Google Scholar
  6. Z. Fu, Q. Sun, Y. Cai, and E. Dai, “Research on forecasting model of forest fire based on Grey-system theory,” Scientia Silvae Sinicae, vol. 38, pp. 95–100, 2002. View at Google Scholar
  7. D. Stojanova, P. Panov, A. Kobler, S. Dzeroski, and K. Taskova, “Learning to predict forest fires with different DataMining techniques,” in Proceedings of the 9th International Multiconference Information Society, Ljubljana, Slovenia, 2006.
  8. P. Cortez and A. Morais, “A data mining approach to predict forest fires using meteorological data,” in Proceedings of the 13th Portuguese Conference on Artificial Intelligence (EPIA '07), Guimarães, Portugal, 2007.
  9. 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, Calif, USA, 1995. View at Google Scholar
  10. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at Scopus
  11. X. Yuan, Y. Zhang, L. Wang, and Y. Yuan, “An enhanced differential evolution algorithm for daily optimal hydro generation scheduling,” Computers and Mathematics with Applications, vol. 55, no. 11, pp. 2458–2468, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. L. D. S. Coelho, J. G. Sauer, and M. Rudek, “Differential evolution optimization combined with chaotic sequences for image contrast enhancement,” Chaos, Solitons & Fractals, vol. 42, no. 1, pp. 522–529, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. H. I. Shaheen, G. I. Rashed, and S. J. Cheng, “Application of differential evolution algorithm for optimal location and parameters setting of UPFC considering power system security,” European Transactions on Electrical Power, vol. 19, no. 7, pp. 911–932, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Das and S. Sil, “Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm,” Information Sciences, vol. 180, no. 8, pp. 1237–1256, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Neri and V. Tirronen, “Recent advances in differential evolution: a survey and experimental analysis,” Artificial Intelligence Review, vol. 33, no. 1-2, pp. 61–106, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Lu, J. Zhou, Y. Li, and H. Qin, “Adaptive differential evolution algorithm combined with chaotic search,” Computer Engineering and Applications Journal, vol. 44, pp. 31–33, 2008. View at Google Scholar
  17. L. Wu, Y. Wang, X. Yuan, and S. Zhou, “Differential evolution algorithm with adaptive second mutation,” Control and Decision, vol. 21, pp. 117–120, 2006. View at Google Scholar
  18. J. Guo, J. Zhou, H. Wang, and Q. Zou, “Structure optimization and parameter calibration of empirical hydrological model under multi-objective framework,” Journal of Hydroelectric Engineering, vol. 33, no. 2, pp. 1–7, 2014. View at Google Scholar · View at Scopus
  19. S. Leva, A. Dolara, F. Grimaccia, M. Mussetta, and E. Ogliari, “Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power,” Mathematics and Computers in Simulation, vol. 131, pp. 88–100, 2017. View at Publisher · View at Google Scholar