dc.contributor.author | Üneş, Fatih | |
dc.contributor.author | Demirci, Mustafa | |
dc.contributor.author | Kaya, Yunus Ziya | |
dc.contributor.author | İspir, Eyüp | |
dc.contributor.author | Mamak, Mustafa | |
dc.date.accessioned | 12.07.201910:50:10 | |
dc.date.accessioned | 2019-07-12T22:02:57Z | |
dc.date.available | 12.07.201910:50:10 | |
dc.date.available | 2019-07-12T22:02:57Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Unes, F., Demirci, M., Kaya, Y. Z., Ispir, E., Mamak, M. (2017). Groundwater level prediction using Support Vektor Machines and autoregressive (AR) modelss. 10th International Conference on Environmental Engineering, ICEE 2017, enviro.2017.093. doi: 10.3846/enviro.2017.093 | en_US |
dc.identifier.uri | https://doi.org/10.3846/enviro.2017.093 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/513 | |
dc.description | 10th International Conference on Environmental Engineering, ICEE 2017 -- 27 April 2017 through 28 April 2017 -- -- 144736 | en_US |
dc.description.abstract | Water resources managers can benefit from accurate prediction of the availability of groundwater. Ground water is a major source of water in Turkey for irrigation, water supply and industrial uses. The ground water level fluctuations depend on several factors such as rainfall, temperature, pumping etc. In this study, Hatay Amik Plain, Kumlu region was evaluated using Autoregressive (AR) and Support Vektor Machines (SVMs) methods. The monthly groundwater level was used the previous years data belonging to the Kumlu region. © 2017 Fatih Üneş, Mustafa Demirci, Yunus Ziya Kaya, Eyup Ispir, Mustafa Mamak. Published by VGTU Press. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY-NC 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Vilnius Gediminas Technical University Publishing House "Technika" | en_US |
dc.relation.isversionof | 10.3846/enviro.2017.093 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Amik Plain | en_US |
dc.subject | Groundwater Level | en_US |
dc.subject | Prediction | en_US |
dc.subject | Support Vektor Machines (SVMs) | en_US |
dc.subject.classification | Artificial neural network | Wavelet | Flood forecasting | en_US |
dc.subject.other | forecasting | en_US |
dc.subject.other | groundwater | en_US |
dc.subject.other | support vector machines | en_US |
dc.subject.other | water levels | en_US |
dc.subject.other | water supply | en_US |
dc.subject.other | accurate prediction | en_US |
dc.subject.other | amik plain | en_US |
dc.subject.other | auto-regressive | en_US |
dc.subject.other | industrial use | en_US |
dc.subject.other | previous year | en_US |
dc.subject.other | source of waters | en_US |
dc.subject.other | groundwater resources | en_US |
dc.subject.other | engineering | en_US |
dc.title | Groundwater level prediction using Support Vektor Machines and autoregressive (AR) modelss | en_US |
dc.type | conferenceObject | en_US |
dc.relation.journal | 10th International Conference on Environmental Engineering, ICEE 2017 | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Üneş, Fatih | |
dc.contributor.isteauthor | Demirci, Mustafa | |
dc.contributor.isteauthor | İspir, Eyüp | |
dc.relation.index | Scopus | en_US |