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/).
Cite this article
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 -