Monthly streamflow prediction and performance comparison of machine learning and deep learning methods

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dc.authoridTURHAN, Evren/0000-0002-0742-4848
dc.authoridAyana, Omer/0000-0002-8896-6509
dc.authoridDENIZ FURKAN, KANBAK/0000-0002-7125-9103
dc.contributor.authorAyana, Omer
dc.contributor.authorKanbak, Deniz Furkan
dc.contributor.authorKeles, Muemine Kaya
dc.contributor.authorTurhan, Evren
dc.date.accessioned2025-01-06T17:36:30Z
dc.date.available2025-01-06T17:36:30Z
dc.date.issued2023
dc.description.abstractStreamflow prediction is an important matter for the water resources management and the design of hydraulic structures that can be built on rivers. Recently, it has become a widely studied research field where data obtained from stream gauge stations can be utilized for creating estimating models by resorting to different methods such as machine and deep learning techniques. In this study, we performed monthly streamflow predictions by using the following data-driven methods of machine learning: linear regression, support vector regression, random forest and deep learning (DL) models to compare the performances of ML's and DL's techniques. A general workflow that can be applied to similar regions is presented. An estimating model containing six-input combinations and time-lagged streamflow data is improved by means of the autocorrelation function (ACF) and partial autocorrelation function (PACF). Furthermore, moving average is used as a smoothing technique to make the dataset more stable and reduce the effects of noise data. A comparative evaluation has been conducted to determine the performances of the above-mentioned methods. In this study, we proposed four different DL models and compared them with existing techniques. For the comparison of the results, we used evaluation criteria such as Nash-Sutcliffe efficiency (NSE), mean square error (MSE) and percent bias (PBIAS). The experimental results indicate that our bidirectional gated recurrent units (BiGRU) model outperforms both ML algorithms and existing solutions with 0.971 NSE, 0.001 MSE and - 1.536 PBIAS scores.
dc.identifier.doi10.1007/s11600-023-01023-6
dc.identifier.endpage2922
dc.identifier.issn1895-6572
dc.identifier.issn1895-7455
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85146857502
dc.identifier.scopusqualityQ2
dc.identifier.startpage2905
dc.identifier.urihttps://doi.org/10.1007/s11600-023-01023-6
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1906
dc.identifier.volume71
dc.identifier.wosWOS:000922301700002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Int Publ Ag
dc.relation.ispartofActa Geophysica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectAutocorrelation function (ACF)-partial autocorrelation function (PACF)
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectMoving average
dc.subjectStreamflow prediction
dc.titleMonthly streamflow prediction and performance comparison of machine learning and deep learning methods
dc.typeArticle

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