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

Ayana, Ömer; Kanbak, Deniz Furkan; Keleş, Mümine Kaya; Turhan, Evren

doi:10.1007/s11600-023-01023-6

Artykuł - szczegóły

Tytuł artykułu

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

Autorzy

Ayana Ömer , Kanbak Deniz Furkan , Keleş Mümine Kaya , Turhan Evren

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-023-01023-6

Warianty tytułu

Języki publikacji

Abstrakty

Streamflow 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.

Słowa kluczowe

autocorrelation function (ACF) partial autocorrelation function (PACF) deep learning machine learning moving average streamfow prediction

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2023

Tom

Vol. 71, no. 6

Strony

2905--2922

Opis fizyczny

Bibliogr. 96 poz., rys., tab.

Twórcy

autor

Ayana Ömer

Department of Software Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

https://orcid.org/0000-0002-8896-6509

autor

Kanbak Deniz Furkan

Department of Software Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

https://orcid.org/0000-0002-7125-9103

autor

Keleş Mümine Kaya

Department of Computer Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

https://orcid.org/0000-0001-8414-1713

autor

Turhan Evren

eturhan@atu.edu.tr

Department of Civil Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

https://orcid.org/0000-0002-0742-4848

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-c36b2f69-a5f7-4270-bb8a-a5b7152aa91f