Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)

Research on flood destruction forecast of Western Zhejiang rural highway based on GA-BP neural network method

Authors
Baofeng Wang
Corresponding Author
Baofeng Wang
Available Online December 2016.
DOI
10.2991/iceeecs-16.2016.32How to use a DOI?
Keywords
BP neural network; rural highway; GA
Abstract

Using genetic algorithm to optimize BP neural network can solve the problem of slow training speed of BP neural network and easy to fall into local minimum points. GA-BP neural network model for forecasting rural highway flood destruction can realize the effective combination of global optimization and local optimization, greatly improving the learning performance and the convergence of flood destruction forecast model. It is proved that the GA-BP neural network has fast calculation speed, and excellent flood destruction forecast function for rural highway.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-265-7
ISSN
2352-538X
DOI
10.2991/iceeecs-16.2016.32How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Baofeng Wang
PY  - 2016/12
DA  - 2016/12
TI  - Research on flood destruction forecast of Western Zhejiang rural highway based on GA-BP neural network method
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 143
EP  - 148
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.32
DO  - 10.2991/iceeecs-16.2016.32
ID  - Wang2016/12
ER  -