Please use this identifier to cite or link to this item:
|Title:||Flood stage forecasting with support vector machines|
|Authors:||Liong, S.-Y. |
Structural risk minimization
Support vector machines
|Source:||Liong, S.-Y.,Sivapragasam, C. (2002). Flood stage forecasting with support vector machines. Journal of the American Water Resources Association 38 (1) : 173-186. ScholarBank@NUS Repository.|
|Abstract:||Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one-lead day to seven-lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four-lead day to seven-lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.|
|Source Title:||Journal of the American Water Resources Association|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 8, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.