Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin

Abda, Zaki; Zerouali, Bilel; Alqurashi, Muwaffaq; Chettih, Mohamed; Santos, Celso Augusto Guimarães; Hussein, Enas E.

doi:10.3390/w13243539

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Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin

^1,*

^2,*

Celso Augusto Guimarães Santos

⁴

and

Enas E. Hussein

⁵

Research Laboratory of Water Resources, Soil and Environment, Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Amar Telidji University, P.O. Box 37.G, Laghouat 03000, Algeria

Vegetable Chemistry-Water-Energy Research Laboratory, Faculty of Civil Engineering and Architecture, Hassiba Benbouali, University of Chlef, B.P. 78C, Ouled Fares, Chlef 02180, Algeria

Department of Civil Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia

⁴

Department of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa 58051-900, Brazil

⁵

Water Research Center, P.O. Box 74, Shubra El-Kheima 13411, Egypt

Authors to whom correspondence should be addressed.

Academic Editor: Giuseppe Pezzinga

Water 2021, 13(24), 3539; https://doi.org/10.3390/w13243539

Received: 14 November 2021 / Revised: 6 December 2021 / Accepted: 8 December 2021 / Published: 10 December 2021

(This article belongs to the Special Issue Advanced Research on Sustainable Water Resources Management and Planning under Climate Change)

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Abstract

Sediment transport in rivers is a nonlinear natural phenomenon, which can harm the environment and hydraulic structures and is one of the main reasons for the dams’ siltation. In this paper, the following artificial intelligence approaches were used to simulate the suspended sediment load (SSL) during periods of flood events in the northeastern Algerian river basins: artificial neural network combined with particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system combined with particle swarm optimization (ANFIS-PSO), random forest (RF), and long short-term memory (LSTM). The comparison of the prediction accuracies of such different intelligent system approaches revealed that ANN-PSO, RF, and LSTM satisfactorily simulated the nonlinear process of SSL. Carefully comparing the results, the ANN-PSO model showed a slight superiority over the RF and LSTM models, with RMSE = 67.2990 kg/s in the Chemourah basin and RMSE = 55.8737 kg/s in the Gareat el tarf basin. View Full-Text

Keywords: SSL; artificial intelligence; LSTM; PSO; ANFIS; random forest

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MDPI and ACS Style

Abda, Z.; Zerouali, B.; Alqurashi, M.; Chettih, M.; Santos, C.A.G.; Hussein, E.E. Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin. Water 2021, 13, 3539. https://doi.org/10.3390/w13243539

AMA Style

Abda Z, Zerouali B, Alqurashi M, Chettih M, Santos CAG, Hussein EE. Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin. Water. 2021; 13(24):3539. https://doi.org/10.3390/w13243539

Chicago/Turabian Style

Abda, Zaki, Bilel Zerouali, Muwaffaq Alqurashi, Mohamed Chettih, Celso A.G. Santos, and Enas E. Hussein. 2021. "Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin" Water 13, no. 24: 3539. https://doi.org/10.3390/w13243539

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Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin

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