dc.contributor.author | Üneş, Fatih | |
dc.contributor.author | Demirci, Mustafa | |
dc.contributor.author | Zelenakova, Martina | |
dc.contributor.author | Çalışıcı, Mustafa | |
dc.contributor.author | Taşar, Bestami | |
dc.contributor.author | Vranay, František | |
dc.contributor.author | Kaya, Yunus Ziya | |
dc.date.accessioned | 2020-12-03T11:59:43Z | |
dc.date.available | 2020-12-03T11:59:43Z | |
dc.date.issued | 2020 | en_US |
dc.identifier.citation | Üneş, 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/w12092427 | en_US |
dc.identifier.uri | https://doi.org/10.3390/w12092427 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/1444 | |
dc.description.abstract | Accurate 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.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.relation.isversionof | 10.3390/w12092427 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Fuzzy logic | en_US |
dc.subject | M5 decision tree | en_US |
dc.subject | Prediction | en_US |
dc.subject | River flow | en_US |
dc.subject | Smrgt | en_US |
dc.subject.classification | Water Resources | |
dc.subject.classification | Stream Flow | Flood Forecasting | Water Tables | |
dc.subject.other | Neural-networks | |
dc.subject.other | Linguistic-synthesis | |
dc.subject.other | M5 tree | |
dc.subject.other | Runoff | |
dc.subject.other | Discharge | |
dc.subject.other | Model | |
dc.subject.other | Identification | |
dc.subject.other | Prediction | |
dc.subject.other | Logic | |
dc.subject.other | Ann | |
dc.subject.other | Atmospheric movements | |
dc.subject.other | Computer circuits | |
dc.subject.other | Decision trees | |
dc.subject.other | Disaster prevention | |
dc.subject.other | Error statistics | |
dc.subject.other | Fuzzy logic | |
dc.subject.other | Fuzzy neural networks | |
dc.subject.other | Fuzzy rules | |
dc.subject.other | Mean square error | |
dc.subject.other | Membership functions | |
dc.subject.other | Rivers | |
dc.subject.other | Stream flow | |
dc.subject.other | Adaptive neuro-fuzzy inference system | |
dc.subject.other | Comparison criterion | |
dc.subject.other | Correlation coefficient | |
dc.subject.other | Efficient use of water | |
dc.subject.other | Mean absolute error | |
dc.subject.other | Multi-linear regression | |
dc.subject.other | Statistical approach | |
dc.subject.other | Water temperature data | |
dc.subject.other | Fuzzy inference | |
dc.subject.other | Disaster management | |
dc.subject.other | Flood | |
dc.subject.other | Natural disaster | |
dc.subject.other | Rainfall | |
dc.subject.other | Resource management | |
dc.subject.other | Water resource | |
dc.subject.other | Water temperature | |
dc.subject.other | United States | |
dc.title | River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques | en_US |
dc.type | article | en_US |
dc.relation.journal | Water (Switzerland) | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- İnşaat Mühendisliği Bölümü | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 9 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Üneş, Fatih | |
dc.contributor.isteauthor | Demirci, Mustafa | |
dc.contributor.isteauthor | Çalışıcı, Mustafa | |
dc.contributor.isteauthor | Taşar, Bestami | |
dc.relation.index | Web of Science - Scopus | en_US |
dc.relation.index | Web of Science Core Collection - Science Citation Index Expanded | |