engleski

Artificial Neural Network Combined with Imperialist Competitive Algorithm for Determination of River Sediments

Estimation of sediment volume transported by a river has become an important water engineering issue. Due to the lack of exact and detailed information on the parameters affecting the non-linear nature of sedimentation process including spatial and temporal variances, a comprehensive sedimentation model cannot be formulated. The new evolving technique of utilizing artificial neural networks, which is based on an optimization algorithm, has found vast applications in different scientific fields, especially in water and river engineering. The Imperialist Competitive Algorithm (ICA) is based on random populations and the idea of the human's socio-political evolution. In this algorithm, a number of imperialist countries and their colonies search for a generalized optimization method for finding optimizing solutions. This research is based on the Feed Forward Artificial Neural Network (FF-ANN) model and attempts to predict and determine sedimentation in rivers. One of the elements used as a new method is employment of ICA for finding the optimized values within ANNs, which is also used for predicting river sedimentations of Karoon River in Ahvaz, Iran. For this purpose, discharge, month of year, height, and density coefficient are the input parameters, and sedimentation estimation is the output value. To determine accuracy of the FF- ICA model, it was compared with genetic and particle swarm group algorithms. This comparison was carried out in three stages of investigation, training, and testing. The results state that the ANN with its weights optimized within ICA, when compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm, has greater flexibility and accuracy in predicting river sedimentation.

Imperialist Competitive Algorithm (ICA) ; Artificial Neural Network (ANN) ; Sedimentation ; Karoon River ; Genetic Algorithm (GA) ; PSO Algorithm

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