Using Neural Networks Forecast the Economic Losses Caused by Storm Surge

Ling Di Zhao; Ming Ye Yang; Chun Peng Bian; Qing Hao

doi:10.4028/www.scientific.net/AMR.798-799.987

Paper Titles

Study on Supply Chain Oriented Service Quality Evaluation of E-Commerce
p.971

The Construction Enterprises Culture Building Based on Knowledge Management
p.975

Application of ARIMA Model in Short-Term Prediction of International Crude Oil Price
p.979

Strategy Study on China E-Business Service in Big Data Era
p.983

Using Neural Networks Forecast the Economic Losses Caused by Storm Surge
p.987

History, Current Situation and Development Prospect of the TRIZ
p.992

Study on Mechanism of Regional Economy Integrated Development in Central Plains Economic Zone
p.998

Production of Recombinant Melittin by Auto-Induction in Escherichia coli
p.1007

A Study about the Influence of Microorganisms on the Properties of D301 Resin Adsorption Catechol
p.1013

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 798-799Using Neural Networks Forecast the Economic Losses...

Using Neural Networks Forecast the Economic Losses Caused by Storm Surge

Abstract:

In order to make up for the lack of natural grade warning, we sought a new method for judging the losses of storm surges. Firstly apply entropy method etc to grade storm surges into 4 levels (mild, moderate, heavy and extra heavy) according to economic loss indices in Zhejiang Province. Then develop BP neural network to forecast losses with the selected indicators of natural, social and economic conditions. Comparing forecast grades with the actual value, we found the accuracy of grade prediction is 80%. It shown the grading results and predicting method are reliable and could be used for the grades of economic losses forecast of storm surges in future.

You might also be interested in these eBooks

Advances in Applied Science and Industrial Technology

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 798-799)

Pages:

987-991

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.798-799.987

Citation:

Cite this paper

Online since:

September 2013

Authors:

Ling Di Zhao, Ming Ye Yang, Chun Peng Bian, Qing Hao

Keywords:

BP Neural Network (BPNN), Economic Loss, Forecast, Grading, Storm Surge

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Paolo Antonio Pirazzoli, Flood threat anomaly for the low coastal areas of the English Channel based on analysis of recent characteristic flood occurrences, Ocean Dynamics, No. 57, 2007, pp.501-510.

DOI: 10.1007/s10236-007-0108-3

Google Scholar

[2] Wang Shengan; You Xiaomin and Li Manqiu, A field experiment of real-time remote monitoring system for nearshore sea wave, storm surge and tsunami and its application, Journal of Tropical Oceanography, No. 28, 2009, pp.29-33.

Google Scholar