Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

Qing Hua Luan; Yao Cheng; Zha Xin Ima

doi:10.4028/www.scientific.net/AMM.39.555

Paper Titles

An Effective Hybrid Collaborative Recommendation Algorithm for Alleviating Data Sparsity
p.535

Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering
p.540

Adaptive Cruise Control Using Sensor Fault-Tolerant Control
p.545

Fatigue Analysis of Diesel Engine Connecting Rod Based on Finite Element Method
p.550

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China
p.555

A New Method of Multiple Factors Analysis of Cell Coverage Based on GIS
p.562

Research on Investment Decision-Making of the Low-Carbon Industrial Listed Company Based on the Uncertainty Set Pair Analysis
p.568

Evolutionary Game Theory-Based Modeling and Stability Analysis of Subsidy Policy on Safety Investment in SMEs
p.575

Privacy Preserving Coordinating Model in Lot to Lot Supply Chain Based on Common Replenishment Epoch
p.580

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 39Test on Flood Prediction-Model Using Artificial...

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

Abstract:

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 39)

Pages:

555-561

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.39.555

Citation:

Cite this paper

Online since:

November 2010

Authors:

Qing Hua Luan, Yao Cheng, Zha Xin Ima

Keywords:

Artificial Neural Network (ANN), Back Propagation Algorithm (BPA), Flood Prediction, Model Test, Simulation Model

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Herbert Coleridge, Frederick James Furnivall, James Murray, et al: Oxford English dictionary (Oxford University Press, Oxford1986).

Google Scholar

[2] Information on http: /baike. baidu. com/view/26292. htm.

Google Scholar

[3] Information on http: /en. wikipedia. org/wiki/Artificial_neural_network.

Google Scholar

[4] Lawrence, Jeanette: Introduction to Neural Networks (Scientific Software Press, California1994).

Google Scholar

[5] Martin T. Hagan, Howard B. Demuch, Mark H. Beale. Neural. Neural Network Design (MA: PWS Publishing, Boston1996).

Google Scholar

[6] Information on http: /en. wikipedia. org/wiki/Backpropagation#Algorithm.

Google Scholar

[7] Siegelmann, H.T. and Sontag, E.D.: Analog computation via neural networks, Theoretical Computer Science, v. 131(1994), no. 2, p.331–360.

DOI: 10.1016/0304-3975(94)90178-3

Google Scholar

[8] Xiaofang Rui: The theory of Hydrology (China Waterpower Press, Beijing2004). (In Chinese).

Google Scholar

[9] Roman M. Balabin, Ekaterina I. Lomakina, Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies J. Chem. Phys. Vol. 131, No. 7: 074104 (2009). doi: 10. 1063/1. 3206326.

DOI: 10.1063/1.3206326

Google Scholar

[10] The Ministry of Water Resources of People's Republic of China, Standard for hydrological information and hydrological forecasting (People's Medical Publishing House, Beijing2001). (In Chinese).

Google Scholar