A Nonlinear Heuristic-Optimized Neural Network Modeling Approach for the Prediction of Natural Disasters

Yu Qiong Wan; Jun You Liang

doi:10.4028/www.scientific.net/AMR.694-697.1310

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697A Nonlinear Heuristic-Optimized Neural Network...

A Nonlinear Heuristic-Optimized Neural Network Modeling Approach for the Prediction of Natural Disasters

Abstract:

The disaster early-warning and corresponding prediction based on the nonlinear models is one of the important research aspects in the scope of natural disaster prediction and prevention. In this paper, the principle of applying heuristic algorithms to modify the classical neural networks so that some improved models with more efficiency can be achieved is proposed. The detailed structure and implementation of such model is also provided by building a heuristic-optimized neural network (HNN) model. Some regional flood data of China is then applied to conduct the prediction with the proposed model. Simulation results demonstrate that the proposed model can achieve higher accuracy and find an appropriate trade-off between the cost of processing time and precision, compared to the normal nonlinear models.

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Periodical:

Advanced Materials Research (Volumes 694-697)

Pages:

1310-1316

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.694-697.1310

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Online since:

May 2013

Authors:

Yu Qiong Wan, Jun You Liang

Keywords:

Genetic Programming, Natural Disaster, Neural Network (NN), Prediction

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References

[1] H.-C. Zhou, and D. Zhang, Transaction of the Chinese Society of Agricultural Engineering (CSAE), vol. 25(2009), p.56.

Google Scholar

[2] S.-Y. Liang, X.-X. Ma, and H.-F. Zhang, Journal of Computers, vol. 5(2010), p.139.

Google Scholar

[3] Z.-X. Xing, and Q. Fu, The 5th International Conference on Natural Computing (ICNC2009), vol. 2(2009), p.366.

Google Scholar

[4] H.-Q. Wang, C. Qin, and W.-W. Zhang, in Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 6(2010), p.2950.

Google Scholar

[5] M. F. Mysorewala, D. O. Popa, and F. L. Lewis, Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 54(2009), p.535.

Google Scholar

[6] G. Andrienko, N. Andrienko, and U. Bartling, Information Visualization, vol. 7(2008), p.89.

Google Scholar

[7] T. Gomez, M. Hernandez, M. A. Leon, and R. Caballero, Forest Ecology and Management, vol. 227(2006), p.79.

Google Scholar

[8] S. S. Durbha, R. L. King, and N. H. Younan, IEEE Geoscience and Remote Sensing Letters, vol. 7(2010), p.43.

Google Scholar