Artificial Neural Networks vs Long Short-Term Memory Prediction of Solid Flow in Tafna Basin (North-West Algeria)

• Autorzy: Medfouni Mohamed Nadjib , Korichi Khaled , Marouf Nadir

Identyfikatory

DOI

10.12912/27197050/181208

• Języki publikacji: EN

Abstrakty

EN

The main objective of this work is to select the most reliable machine learning model to predict the generated solid flow in the Tafna basin (North-West of Algeria). It is about the artificial neural networks (ANN) and long short-term memory (LSTM). The sediment load is recorded through three hydrometric stations. The efficiency and performance of the two models is verified using the correlation coefficient (R2), the Nash-Sutcliffe coefficient (NSC) and the root mean square error (RMSE). The obtained simulated solids load shows a very good correlation in terms of precision although the ANN model gave relatively better results compared to the LSTM model where low RMSE values were recorded, which confirms that the artificial intelligence models remain also effective for the treatment and the prediction of hydrological phenomena such as the estimation of the solid load in a such watershed.

Słowa kluczowe

EN

solid flow; Tafna basin; ANN; LSTM; prediction; performance

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 3
• Strony: 275--286
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Medfouni Mohamed Nadjib

• Civil Engineering and Environment Laboratory, Djillali Liabes University of Sidi Bel Abbes, Bp 89 Sidi Bel Abbes 22000, Algeria

• https://orcid.org/0000-0002-1452-1910

autor

Korichi Khaled

• Civil Engineering and Environment Laboratory, Djillali Liabes University of Sidi Bel Abbes, Bp 89 Sidi Bel Abbes 22000, Algeria

• https://orcid.org/0000-0002-2663-2757

autor

Marouf Nadir

• Laboratory of Ecology and Environment, University of Larbi Ben M’hidi, University of Larbi Ben M’hidi, Oum El Bouaghi 04000, Algeria

• https://orcid.org/0000-0002-9359-0323

Bibliografia

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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).