River flow estimation from upstream flow records by artificial intelligence methods
| dc.contributor.author | Turan, ME | |
| dc.contributor.author | Yurdusev, MA | |
| dc.date.accessioned | 2024-07-18T11:47:01Z | |
| dc.date.available | 2024-07-18T11:47:01Z | |
| dc.description.abstract | Water resources management has become more and more crucial by the depletion of available water resources to use as opposed to the increase of the water consumption. An effective management relies on accurate and complete information about the river on which a project will be constructed. Artificial intelligence techniques are often and successfully used to complete the unmeasured data. In this study, feed forward back propagation neural networks, generalized regression neural network, fuzzy logic are used to estimate unmeasured data using the data of the four runoff gauge station oil the Birs River in Switzerland. The performances of these models are measured by the mean square error, determination coefficients and efficiency coefficients to choose the best fit model. (C) 2009 Elsevier B.V. All rights reserved. | |
| dc.identifier.issn | 0022-1694 | |
| dc.identifier.other | 1879-2707 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/3176 | |
| dc.language.iso | English | |
| dc.publisher | ELSEVIER | |
| dc.subject | NEURAL-NETWORKS | |
| dc.subject | FUZZY-LOGIC | |
| dc.subject | ALGORITHM | |
| dc.subject | MODEL | |
| dc.title | River flow estimation from upstream flow records by artificial intelligence methods | |
| dc.type | Article |