Abstract
This paper presents a Least-Square Support Vector Machine (LS-SVM) approach for forecasting nonlinear hydrological time series. LS-SVM is a machine-learning algorithm firmly based on the statistical learning theory. The objective of this paper is to examine the feasibility of using LS-SVM in the forecasting of nonlinear hydrological time series by comparing it with a statistical method such as Multiple Linear Regression (MLR) and a heuristic method such as a Neural Network using Back-Propagation (NNBP). And the performance of prediction model is also dependent on the degrees of linearity (or persistency) of data, not only on the performance of model itself. Thus, we would clearly verify that prediction performance of three models according to linear extent using daily water demand and daily inflow of dam data. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to those of MLR and NNBP and LS-SVM demonstrated better forecasting efficiency in nonlinear hydrological time series using Relative Correlation Coefficient (RCC) which is a relative measure of forecasting efficiency with different persistency.
| Original language | English |
|---|---|
| Pages (from-to) | 870-882 |
| Number of pages | 13 |
| Journal | KSCE Journal of Civil Engineering |
| Volume | 16 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2012 Jul 1 |
Fingerprint
Keywords
- forecasting
- forecasting performance
- support vector machine
ASJC Scopus subject areas
- Civil and Structural Engineering
Cite this
Forecasting performance of LS-SVM for nonlinear hydrological time series. / Hwang, Seok Hwan; Ham, Dae Heon; Kim, Joong Hoon.
In: KSCE Journal of Civil Engineering, Vol. 16, No. 5, 01.07.2012, p. 870-882.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Forecasting performance of LS-SVM for nonlinear hydrological time series
AU - Hwang, Seok Hwan
AU - Ham, Dae Heon
AU - Kim, Joong Hoon
PY - 2012/7/1
Y1 - 2012/7/1
N2 - This paper presents a Least-Square Support Vector Machine (LS-SVM) approach for forecasting nonlinear hydrological time series. LS-SVM is a machine-learning algorithm firmly based on the statistical learning theory. The objective of this paper is to examine the feasibility of using LS-SVM in the forecasting of nonlinear hydrological time series by comparing it with a statistical method such as Multiple Linear Regression (MLR) and a heuristic method such as a Neural Network using Back-Propagation (NNBP). And the performance of prediction model is also dependent on the degrees of linearity (or persistency) of data, not only on the performance of model itself. Thus, we would clearly verify that prediction performance of three models according to linear extent using daily water demand and daily inflow of dam data. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to those of MLR and NNBP and LS-SVM demonstrated better forecasting efficiency in nonlinear hydrological time series using Relative Correlation Coefficient (RCC) which is a relative measure of forecasting efficiency with different persistency.
AB - This paper presents a Least-Square Support Vector Machine (LS-SVM) approach for forecasting nonlinear hydrological time series. LS-SVM is a machine-learning algorithm firmly based on the statistical learning theory. The objective of this paper is to examine the feasibility of using LS-SVM in the forecasting of nonlinear hydrological time series by comparing it with a statistical method such as Multiple Linear Regression (MLR) and a heuristic method such as a Neural Network using Back-Propagation (NNBP). And the performance of prediction model is also dependent on the degrees of linearity (or persistency) of data, not only on the performance of model itself. Thus, we would clearly verify that prediction performance of three models according to linear extent using daily water demand and daily inflow of dam data. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to those of MLR and NNBP and LS-SVM demonstrated better forecasting efficiency in nonlinear hydrological time series using Relative Correlation Coefficient (RCC) which is a relative measure of forecasting efficiency with different persistency.
KW - forecasting
KW - forecasting performance
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84863306895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863306895&partnerID=8YFLogxK
U2 - 10.1007/s12205-012-1519-3
DO - 10.1007/s12205-012-1519-3
M3 - Article
AN - SCOPUS:84863306895
VL - 16
SP - 870
EP - 882
JO - KSCE Journal of Civil Engineering
JF - KSCE Journal of Civil Engineering
SN - 1226-7988
IS - 5
ER -