Basit öğe kaydını göster

dc.contributor.authorÜneş, Fatih
dc.contributor.authorDemirci, Mustafa
dc.contributor.authorZelenakova, Martina
dc.contributor.authorÇalışıcı, Mustafa
dc.contributor.authorTaşar, Bestami
dc.contributor.authorVranay, František
dc.contributor.authorKaya, Yunus Ziya
dc.date.accessioned2020-12-03T11:59:43Z
dc.date.available2020-12-03T11:59:43Z
dc.date.issued2020en_US
dc.identifier.citationÜneş, F., Demirci, M., Zelenakova, M., Çalişici, M., Taşar, B., Vranay, F., Ziya Kaya, Y. (2020). River flow estimation using artificial intelligence and fuzzy techniques. Water (Switzerland), 12 (9), art. no. 2427. https://doi.org/10.3390/w12092427en_US
dc.identifier.urihttps://doi.org/10.3390/w12092427
dc.identifier.urihttps://hdl.handle.net/20.500.12508/1444
dc.description.abstractAccurate determination of river flows and variations is used for the efficient use of water resources, the planning of construction of water structures, and preventing flood disasters. However, accurate flow prediction is related to a good understanding of the hydrological and meteorological characteristics of the river basin. In this study, flow in the river was estimated using Multi Linear Regression (MLR), Artificial Neural Network (ANN), M5 Decision Tree (M5T), Adaptive Neuro-Fuzzy Inference System (ANFIS), Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) models. The Stilwater River in the Sterling region of the USA was selected as the study area and the data obtained from this region were used. Daily rainfall, river flow, and water temperature data were used as input data in all models. In the paper, the performance of the methods is evaluated based on the statistical approach. The results obtained from the generated models were compared with the recorded values. The correlation coefficient (R), Mean Square Error (MSE), and Mean Absolute Error (MAE) statistics are computed separately for each model. According to the comparison criteria, as a final result, it is considered that Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model have better performance in river flow estimation than the other models.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.isversionof10.3390/w12092427en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectFuzzy logicen_US
dc.subjectM5 decision treeen_US
dc.subjectPredictionen_US
dc.subjectRiver flowen_US
dc.subjectSmrgten_US
dc.subject.classificationWater Resources
dc.subject.classificationStream Flow | Flood Forecasting | Water Tables
dc.subject.otherNeural-networks
dc.subject.otherLinguistic-synthesis
dc.subject.otherM5 tree
dc.subject.otherRunoff
dc.subject.otherDischarge
dc.subject.otherModel
dc.subject.otherIdentification
dc.subject.otherPrediction
dc.subject.otherLogic
dc.subject.otherAnn
dc.subject.otherAtmospheric movements
dc.subject.otherComputer circuits
dc.subject.otherDecision trees
dc.subject.otherDisaster prevention
dc.subject.otherError statistics
dc.subject.otherFuzzy logic
dc.subject.otherFuzzy neural networks
dc.subject.otherFuzzy rules
dc.subject.otherMean square error
dc.subject.otherMembership functions
dc.subject.otherRivers
dc.subject.otherStream flow
dc.subject.otherAdaptive neuro-fuzzy inference system
dc.subject.otherComparison criterion
dc.subject.otherCorrelation coefficient
dc.subject.otherEfficient use of water
dc.subject.otherMean absolute error
dc.subject.otherMulti-linear regression
dc.subject.otherStatistical approach
dc.subject.otherWater temperature data
dc.subject.otherFuzzy inference
dc.subject.otherDisaster management
dc.subject.otherFlood
dc.subject.otherNatural disaster
dc.subject.otherRainfall
dc.subject.otherResource management
dc.subject.otherWater resource
dc.subject.otherWater temperature
dc.subject.otherUnited States
dc.titleRiver Flow Estimation Using Artificial Intelligence and Fuzzy Techniquesen_US
dc.typearticleen_US
dc.relation.journalWater (Switzerland)en_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- İnşaat Mühendisliği Bölümüen_US
dc.identifier.volume12en_US
dc.identifier.issue9en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorÜneş, Fatih
dc.contributor.isteauthorDemirci, Mustafa
dc.contributor.isteauthorÇalışıcı, Mustafa
dc.contributor.isteauthorTaşar, Bestami
dc.relation.indexWeb of Science - Scopusen_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded


Bu öğenin dosyaları:

Thumbnail

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster