2026-10-19T01:52:55Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/123202026-10-18T21:13:20Zopenairedrivercom_ebyu_10000col_ebyu_10001

Application of deep learning approaches to predict monthly stream flowsDalkilic, H. YildirimKumar, DeepakSamui, PijushDixon, BarnaliYesilyurt, S. NurKatipoğlu, O. MertGated recurrent unitLong short-term memoryRecurrent neural networkStreamflow forecastingArtificial IntelligenceDeep LearningEnvironmental MonitoringNeural Networks, ComputerRiversBrainEfficiencyForecastingHydroelectric powerHydroelectric power plantsMean square errorReservoirs (water)Risk managementStream flowWater resourcesDrought risksEffective use of watersFlood risksFlow estimationHydroelectric power generationLearning approachRisks managementWaters resourcesartificial neural networkdroughtestimation methodAccurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gated recurrent unit (GRU) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) to predict with river flows at three different streamflow observation stations in Erzincan, Bayburt, and Gümüshane. Monthly streamflow time series covering the years 1978 to 2015 were used to set up artificial intelligence models. During the modeling phase, 70% of the data was divided into training (October 1978–April 2004), 15% validation (May 2004–September 2009), and 15% test set (October 2010–September 2015). Model performances were made according to the correlation coefficient, root mean square error, the ratio of RMSE to the standard deviation, Nash–Sutcliffe efficiency coefficient, index of agreement, and volumetric efficiency values. The calculation results show that GRU leads efficient estimation results for estimating streamflow and can also be used in allied water resources. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Springer Science and Business Media Deutschland GmbH2023Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/12320https://doi.org/10.1007/s10661-023-11331-50167-6369enEnvironmental Monitoring and Assessmentinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/12320