 |
| Titel |
A hybrid model of self organizing maps and least square support vector machine for river flow forecasting |
| VerfasserIn |
S. Ismail, A. Shabri, R. Samsudin |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 11 ; Nr. 16, no. 11 (2012-11-26), S.4417-4433 |
| Datensatznummer |
250013584
|
| Publikation (Nr.) |
 copernicus.org/hess-16-4417-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
| Successful river flow forecasting is a major goal and an essential procedure
that is necessary in water resource planning and management. There are many
forecasting techniques used for river flow forecasting. This study proposed
a hybrid model based on a combination of two methods: Self Organizing Map
(SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as
the SOM-LSSVM model for river flow forecasting. The hybrid model uses the
SOM algorithm to cluster the entire dataset into several disjointed
clusters, where the monthly river flows data with similar input pattern are
grouped together from a high dimensional input space onto a low dimensional
output layer. By doing this, the data with similar input patterns will be
mapped to neighbouring neurons in the SOM's output layer. After the dataset
has been decomposed into several disjointed clusters, an individual LSSVM is
applied to forecast the river flow. The feasibility of this proposed model
is evaluated with respect to the actual river flow data from the Bernam
River located in Selangor, Malaysia. The performance of the SOM-LSSVM was
compared with other single models such as ARIMA, ANN and LSSVM. The
performance of these models was then evaluated using various performance
indicators. The experimental results show that the SOM-LSSVM model
outperforms the other models and performs better than ANN, LSSVM as well as
ARIMA for river flow forecasting. It also indicates that the proposed model
can forecast more precisely, and provides a promising alternative technique
for river flow forecasting. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|