Application of the Cuckoo Search Based SUPPORT Vector Machine for the Mean Monthly Runoff Forecasting
B. Xing, Y. Wang, G.D. Liu, Y.F. Ren
Available Online November 2015.
- 10.2991/itms-15.2015.74How to use a DOI?
- Mean monthly runoff forecast; support vector machine; cuckoo search; artificial neural network
Support vector machine (SVM) which is at the forefront of current research due to its high accuracy was used in this paper to carry out mean monthly runoff forecasting. Cuckoo search (CS) was introduced to determine the SVM parameters (kernel parameter ( ) and penalty parameter (C)). Mean monthly runoff and monthly precipitation from 1952 to 2011 of Yichang station in the upper reaches of the Yangtze River were trained and tested. In order to evaluate the effectiveness of the proposed model, the data sets were also modeled using Artificial Neural Networks (ANN). The results indicate that the proposed model (cuckoo search based SVM) is more accurate compared to ANN. This study suggests new opportunities for runoff forecasting.
- © 2015, 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 - B. Xing AU - Y. Wang AU - G.D. Liu AU - Y.F. Ren PY - 2015/11 DA - 2015/11 TI - Application of the Cuckoo Search Based SUPPORT Vector Machine for the Mean Monthly Runoff Forecasting BT - Proceedings of the 2015 International Conference on Industrial Technology and Management Science PB - Atlantis Press SP - 311 EP - 315 SN - 2352-538X UR - https://doi.org/10.2991/itms-15.2015.74 DO - 10.2991/itms-15.2015.74 ID - Xing2015/11 ER -