Storm surge disaster evaluation model based on an artificial neural network

Fang Ji; Yijun Hou

doi:10.1007/s00343-011-1042-2

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Storm surge disaster evaluation model based on an artificial neural network

Updated：2023-05-16

- Storm surge disaster evaluation model based on an artificial neural network
- Journal of Oceanology and Limnology Vol. 29, Issue 5, Pages: 1142-1146(2011)
- Affiliations：
  
  1. Institute of Oceanology Chinese Academy of Sciences
  2. Graduate University of Chinese Academy of Sciences
- Author bio：
- Funds：
- DOI：10.1007/s00343-011-1042-2
  CLC：
- Online First：08 August 2011，
  
  Published：2011
- Accepted：
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Ji, F., Hou, Y. Storm surge disaster evaluation model based on an artificial neural network., Chin. J. Ocean. Limnol. 29, 1142–1146 (2011). https://doi.org/10.1007/s00343-011-1042-2
DOI：

Ji, F., Hou, Y. Storm surge disaster evaluation model based on an artificial neural network., Chin. J. Ocean. Limnol. 29, 1142–1146 (2011). https://doi.org/10.1007/s00343-011-1042-2 DOI：

摘要

Abstract

Back propagation is employed to forecast the current of a storm with various characteristics of storm surge; the technique is thus important in disaster forecasting. One of the most fuzzy types of information in the prediction of geological calamity is handled employing the information diffusion method. First

a single-step prediction model and neural network prediction model are employed to collect influential information used to predict the extreme tide level. Second

information is obtained using the information diffusion method

which improves the precision of risk recognition when there is insufficient information. Experiments demonstrate that the method proposed in this paper is simple and effective and provides better forecast results than other methods. Future work will focus on a more precise forecast model.

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references

Andrews R, Diederich J, Tickle A B. 1995. A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8(6): 373–389.

Benediktsson J A, Swain P H, Ersoy O K. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. Geosci. Remote Sens., 28(4): 540–552.

Chen Q, Wang L X, Zhao H H, Douglass S L. 2007. Prediction of storm surges and wind waves on coastal highways in hurricane-prone areas. Journal of Coastal Research, 23(5): 1 304–1 317.

Guler I, Gokcil Z, Gulbandilar E. 2009. Evaluating of traumatic brain injuries using artificial neural networks. Expert System with Applications, 36(7): 10 424–10 427.

Huang J C. 2002. On storm surge disasters in China. Water Resources Development Research, 2(12): 63–65. (in Chinese with English abstract)

Mohanty S. 2009. Artificial neural network based system identification and model predictive control of a flotation column. Journal of Process Control, 19(6): 991–999.

Park K-S, Lee J-C, Jun K-C, Kim S-I, Kwon J-I. 2009. Development of an operational storm surge prediction system for the Korean coast. Ocean and Polar Research, 31(4): 283–426.

Storch H, Woth K. 2008. Storm surges: perspectives and options. Sustainability Science, 3(1): 33–43.

Woodworth P L, Flather R A, Williams J A, Wakelin S L, Jevrejeva S. 2007. The dependence of UK extreme sea levels and storm surges on the North Atlantic Oscillation. Continental Shelf Research, 27(7): 935–946.

Yin Q J, Wu S H, Wang X N. 1997. The application of U.S. SLOSH model in China-the simulation of typhoon storm surge in Hangzhou Bay. J. Marine Forecasts, 14(1): 70–74. (in Chinese with English abstract)

Yi X Y, Yu W T, Yan Z C, Li S S. 2006. Several storm surge forecasting methods in the actual use and comparison of prediction. J. Marine Forecasts, 23(4): 82–87. (in Chinese with English abstract)

You S H. 2010. Predicting typhoon induced storm surges using the operational ocean forecast system. Terrestrial, Atmospheric and Oceanic Sciences, 21(1): 99–111.

Zhang Z G, San Y. 2004. Adaptive wavelet neural network for prediction of hourly NOX and No.2 Concentrations. WSC’ 04 Proceedings of the 36th Conference on Winter Simulation. p.1 770–1 778.

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