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

Acta Geophysica

article

Bouguerra, Hamza¹  Tachi, Salah-Eddine²  Derdous, Oussama²  Bouanani, Abderrazak¹  Khanchoul, Kamel⁴ 

[1] Department of Hydraulic, Faculty of Technology, University of Abu Baker Belkaid;Laboratoire de Recherche Science de L’eau, National Polytechnic School;Department of Civil and Hydraulic Engineering, Faculty of Applied Sciences, University of Ouargla;Department of Geology, Faculty of Earth Sciences, University of Badji Mokhtar

关键词: Suspended sediment discharges;    Flood events;    Modeling;    Artificial neural network;    Levenberg–Marquardt algorithm;    Quasi-Newton algorithm;   

DOI  :  10.1007/s11600-019-00373-4

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

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 $$\Delta_{{R^{2} }}$$ ΔR2 4.23% and $$\Delta_{{{\text{RMSE}}}}$$ ΔRMSE 1.74‰, and with the best improvement presented in ANN05 with $$\Delta_{{R^{2} }}$$ ΔR2 6.07% and $$\Delta_{{{\text{RMSE}}}}$$ ΔRMSE 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.

【 授权许可】

Unknown