Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2750

Full Length Research Paper

Comparision of ant colony optimization and genetic algorithm models for identifying the relation between flow discharge and suspended sediment load (Gorgan River - Iran)

Omolbani Mohamad Reza Pour1*, Lee Teang Shui2 and Amir Ahmad Dehghani3
1Zabol University, Zabol, Iran. 2Faculty of Engineering University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia 3Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
Email: [email protected]

  •  Accepted: 30 May 2012
  •  Published: 31 October 2012


Correct estimation of sediment volume carried by a river is very important for many water resources projects. The prediction of river sediment load also constitutes an important issue in hydraulic and river engineering. Conceptual models based on artificial intelligence models, namely, ant colony optimization (ACO) and genetic algorithm (GA) are now being used more frequently to solve optimization problems. Hence, the main purpose of this study was to apply ACO and GA in order to identify the relation between stream flow discharge and sediment loads for Nodeh station at the Gorgan River in Iran. The training and testing data sets were chosen based on the K-fold method of cross validation to find the optimal classifier. Different input combinations of ACO and GA models (that is, ACO1 and GA1: the suspended sediment estimation based on current discharge; ACO2 and GA2: the estimation of suspended sediment based on current, one day of previous discharges; and ACO3 and GA3: the suspended sediment estimation based on current, one and two-day of previous discharges) were chosen based on similar meteorological requirements to those of the suspended sediment equations included in this study. The estimation of the ACO and GA models was also compared with the empirical model, such as the sediment rating curve (SRC) technique. The models were compared based on statistical criteria, namely; regression coefficient (R2), Nash-Sutclif coefficient (CE) and root mean square error (RMSE). The results indicated that the ACO1 model provided better performance in estimating the suspended sediment loads as compared to the ACO models. Also, the GA2 model was more accurate than the GA1 and GA3 models. The findings in this study showed that the performance of the SRC model was more inferior the ACO and GA techniques when the inputs of the GA, ACO and rating curve models comprised only the current discharge. As seen from the results, the ACO1 model approximated that the corresponding observed suspended sediment values were better than the rating curve and GA2 techniques. However, for the peak flow discharge, the GA2 model could predict the suspended sediment better than the ACO2 and SRC models.


Key words: Suspended sediment, rating curve, ant colony optimization, genetic algorithm, Gorgan River, Iran.