Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/5078
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Firat, M. | - |
dc.contributor.author | Güngör, M. | - |
dc.date.accessioned | 2019-08-16T11:41:01Z | - |
dc.date.available | 2019-08-16T11:41:01Z | - |
dc.date.issued | 2004 | - |
dc.identifier.issn | 1300-3453 | - |
dc.identifier.uri | https://hdl.handle.net/11499/5078 | - |
dc.description.abstract | Computation on Civil Engineering has concentrated primarily on artificial intelligence applications in the past few years. These applications generally involve expert systems. This article deals Neural Networks and applications were presented. It quickly gives results In test phase in short time. It is a preferable method among the other approaching methods. In Turkey, sedimentation which is the natural result of erosion occurring by different factors is known having an adverse effect on development of soil and water resources. In this study, the suspended sediment amount carried by stream is determined by the feed forward neural network method. The training sets for the problem were generated through sediment measurements which have been performed by EIE. | en_US |
dc.language.iso | tr | en_US |
dc.relation.ispartof | Teknik Dergi/Technical Journal of Turkish Chamber of Civil Engineers | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Feed forward neural network method | en_US |
dc.subject | Sediment measurements | en_US |
dc.subject | Soil development | en_US |
dc.subject | Suspended sediments | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Calculations | en_US |
dc.subject | Civil engineering | en_US |
dc.subject | Computer applications | en_US |
dc.subject | Erosion | en_US |
dc.subject | Expert systems | en_US |
dc.subject | Problem solving | en_US |
dc.subject | Sedimentation | en_US |
dc.subject | Water resources | en_US |
dc.subject | Neural networks | en_US |
dc.title | Determination of carried suspended sediment concentration and amount by artificial neural networks | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.startpage | 3267 | |
dc.identifier.startpage | 3267 | en_US |
dc.identifier.endpage | 3282 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopus | 2-s2.0-3142663403 | en_US |
dc.identifier.scopusquality | Q3 | - |
dc.owner | Pamukkale_University | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | tr | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 10.02. Civil Engineering | - |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
11
checked on Dec 14, 2024
Page view(s)
54
checked on Aug 24, 2024
Google ScholarTM
Check
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.