| dc.creator |
Terzi, Oezlem |
|
| dc.creator |
Taylan, Dilek |
|
| dc.creator |
Keskin, Mustafa Erol |
|
| dc.date |
2006-07-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T11:22:53Z |
|
| dc.date.available |
2020-10-06T11:22:53Z |
|
| dc.identifier |
c5132863-8d2b-43a2-9d1c-6bb8af54855e |
|
| dc.identifier |
10.1623/hysj.51.4.588 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/c5132863-8d2b-43a2-9d1c-6bb8af54855e/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/71531 |
|
| dc.description |
The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series |
|
| dc.type |
info:eu-repo/semantics/article |
|