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

OriginalPaper | Updated：2023-05-16

- Storm surge disaster evaluation model based on an artificial neural network
- Storm surge disaster evaluation model based on an artificial neural network
- 海洋湖沼学报（英文） 2011年29卷第5期页码：1142-1146
- Affiliations：
  
  1. Institute of Oceanology Chinese Academy of Sciences
  2. Graduate University of Chinese Academy of Sciences
- Author bio：
- Funds：
  
  Supported by the MISSION 908 (Nos. 908-02-03-07, SD-908-02-08)
- DOI：10.1007/s00343-011-1042-2
  中图分类号：
- 网络首发：2011-08-08，
  
  纸质出版：2011
- Accepted：
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Storm surge disaster evaluation model based on an artificial neural network[J]. 海洋湖沼学报（英文）, 2011,29(5):1142-1146.

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
Storm surge disaster evaluation model based on an artificial neural network[J]. 海洋湖沼学报（英文）, 2011,29(5):1142-1146. DOI： 10.1007/s00343-011-1042-2.

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

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