Journal of
Engineering and Technology Research

  • Abbreviation: J. Eng. Technol. Res.
  • Language: English
  • ISSN: 2006-9790
  • DOI: 10.5897/JETR
  • Start Year: 2009
  • Published Articles: 198

Full Length Research Paper

PSO-ANN’s based suspended sediment concentration in Ksob basin, Algeria

Baazi Houria*, Kalla Mahdi and Tebbi Fatima Zohra
Natural Hazards and Territory Planning Laboratory (LRNAT), Hadj Lakhdar University, Batna (UHLB), Algeria.
Email: [email protected]

  •  Received: 19 October 2014
  •  Accepted: 24 November 2014
  •  Published: 28 December 2014


Suspended sediment concentration estimation has a major influence on river basin planning and management. Prediction of such parameter with artificial neural network (ANN) has shown its performance, because of this, Back Propagation Neural Network model trained with particle swarm optimization (PSO) is used to forecast the daily sediment concentration for Ksob river, Tebessa using 22 years data set from Morsott gauging station; the recorded daily suspended sediment concentrations and correspondent daily discharges were used to train the ANN model. PSO is used to allow ANN architecture to be easily optimized. Simulation of both ANN and PSO-ANN models has shown more accurate results compared with the traditional sediment rating curve.


Key words:  Ksob basin, Algeria, sediment rating curve, neural networks, particle swarm optimization.