A Novel Prediction Approach for Runoff Based On Hybrid HMM-SVM Model

DOI

10.2991/icseee-16.2016.23How to use a DOI?

Keywords

Hidden Markov Model, Shape Based Clustering, SVM, Runoff Prediction

Abstract

This research demonstrates an application of Hidden Markov Model (HMM) and Support Vector Machine (SVM) for watershed-runoff forecasts. HMM is used for shape-based clustering by calculating log-likelihood values of each data to identify data in the data set with similar data pattern.Then we put these data into different classes based on their shapes and train their corresponding SVM model to predict the output of the system finally. The applications of daily runoff and monthly runoff are used for testing the competence of this method and experimental results demonstrate that this hybrid HMM-SVM algorithm can meet the prediction requirement and has high prediction accuracy.

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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TY  - CONF
AU  - Feng Chen
AU  - Yongqing Su
AU  - Yin Wang
PY  - 2016/12
DA  - 2016/12
TI  - A Novel Prediction Approach for Runoff Based On Hybrid HMM-SVM Model
BT  - Proceedings of the 2016 5th International Conference on Sustainable Energy and Environment Engineering (ICSEEE 2016)
PB  - Atlantis Press
SP  - 135
EP  - 139
SN  - 2352-5401
UR  - https://doi.org/10.2991/icseee-16.2016.23
DO  - 10.2991/icseee-16.2016.23
ID  - Chen2016/12
ER  -