Proceedings of the 2016 5th International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2016)

Runoff simulation Based on Least Square Support Vector Machine

Authors
Jun Ping Liu, Jun Jie Zhou, Xian Bai Zou
Corresponding Author
Jun Ping Liu
Available Online October 2016.
DOI
https://doi.org/10.2991/iccahe-16.2016.138How to use a DOI?
Keywords
Statistical Learning Theory; Least Square Support Vector Machine; Runoff; Simulation
Abstract

River runoff is highly nonlinear as affected by the combination of climate, underlying surface condition, etc. Prediction of runoff may guide engineering design, construction and operation. Statistical Learning Theory (SLT) studies the rules of machine learning with finite samples. Support Vector Machine (SVM) is a new machine learning method based on Statistical Learning Theory. It is a solution for the highly nonlinear classification and regression in sample space. Map the one-dimensional runoff series input space of one hydrologic station of the Yellow River onto high-dimensional input space. Then calculate the embedding dimension of runoff time series and reconstruct runoff series into three-dimensional phase space. Using radial base kernel function to learn from 83 training samples through grid search method and optimize model parameters to estab-lish the Least Square Support Vector Machine (LSSVM) prediction model of river runoff. Fitting mean-square error of the model is 0.0148. Prediction mean-square error of the model on 20 samples is 0.0120, a correlation coefficient of 0.975 between predicted and measured values. The result shows that the generalization ability of LSSVM model is high and the prediction result is satisfacto-ry..

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 5th International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2016)
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-250-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/iccahe-16.2016.138How 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  - Jun Ping Liu
AU  - Jun Jie Zhou
AU  - Xian Bai Zou
PY  - 2016/10
DA  - 2016/10
TI  - Runoff simulation Based on Least Square Support Vector Machine
BT  - Proceedings of the 2016 5th International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2016)
PB  - Atlantis Press
SP  - 885
EP  - 890
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccahe-16.2016.138
DO  - https://doi.org/10.2991/iccahe-16.2016.138
ID  - Liu2016/10
ER  -