E-resources
Peer reviewed
-
Terzi, Özlem; Ergin, Gülşah
Neural computing & applications, 07/2014, Volume: 25, Issue: 1Journal Article
This study was conducted by using autoregressive (AR) modeling and data-driven techniques which include gene expression programming (GEP), radial basis function network and feed-forward neural networks, and adaptive neural-based fuzzy inference system (ANFIS) techniques to forecast monthly mean flow for Kızılırmak River in Turkey. The lagged monthly river flow measurements from 1955 to 1995 were taken into consideration for development of the models. The correlation coefficient and root-mean-square error performance criteria were used for evaluating the accuracy of the developed models. When the results of developed models were compared with flow measurements using these criteria, it was shown that the AR(2) model gave the best performance among all developed models and the GEP and ANFIS models had good performance in data-driven techniques.
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.