Suspended sediment discharge modeling during flood events using two different artificial neural network algorithms

• Autorzy: Bouguerra, Hamza , Tachi, Salah-Eddine , Derdous, Oussama , Bouanani, Abderrazak , Khanchoul, Kamel
• Języki publikacji: EN

Abstrakty

EN

This paper presents modeling of artificial neural network (ANN) to forecast the suspended sediment discharges (SSD) during flood events in two different catchments in the Seybouse basin, northeastern Algeria. This study was carried out on hourly SSD and water discharge data during flood events from a period of 31 years in the Ressoul catchment and of 28 years in the Mellah catchment. The ANNs were trained according to two different algorithms: the Levenberg–Marquardt algorithm (LM) and the Quasi-Newton algorithm (BFGS). Seven input combinations were trained for the SSD prediction. The performance results indicated that both algorithms provided satisfactory simulations according to the determination coefficient (R²) and root mean squared error (RMSE) performance criteria, with priority to the BFGS algorithm; the coefficient of determination using the LM algorithm varies between 51.0 and 90.2%, whereas using the BFGS algorithm it varies between 54.3 and 93.5% in both studied catchments, with calculated improvement for all seven developed networks with the best improvement in the Ressoul catchment presented in ANN06 with Δ_R2 4.23% and Δ_RMSE 1.74‰, and with the best improvement presented in ANN05 with Δ_R2 6.07% and Δ_RMSE 0.71‰ in the Mellah catchment. The analysis showed that the use of Quasi-Newton method performed better than the Levenberg–Marquardt in both studied areas.

Słowa kluczowe

PL

modelowanie; sztuczna sieć neuronowa; algorytm Levenberga-Marquardta; algorytm quasi-Newtona

EN

modeling; artificial neural network; Levenberg–Marquardt algorithm; quasi-Newton algorithm; suspended sediment discharges; flood event

• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 6
• Strony: 1649--1660
• Opis fizyczny: Bibliogr. 37 poz.

Twórcy

autor

Bouguerra, Hamza

• Department of Hydraulic, Faculty of Technology, University of Abu Baker Belkaid, Tlemcen, Algeria,
• Laboratoire de Recherche Science de L’eau, National Polytechnic School, Algiers, Algeria

autor

Tachi, Salah-Eddine

• Laboratoire de Recherche Science de L’eau, National Polytechnic School, Algiers, Algeria,

autor

Derdous, Oussama

• Laboratoire de Recherche Science de L’eau, National Polytechnic School, Algiers, Algeria,
• Department of Civil and Hydraulic Engineering, Faculty of Applied Sciences, University of Ouargla, Ouargla, Algeria

autor

Bouanani, Abderrazak

• Department of Hydraulic, Faculty of Technology, University of Abu Baker Belkaid, Tlemcen, Algeria,

autor

Khanchoul, Kamel

• Department of Geology, Faculty of Earth Sciences, University of Badji Mokhtar, Annaba, Algeria,

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).

Identyfikatory

DOI

10.1007/s11600-019-00373-4