Evolutionary optimization of neural network to predict sediment transport without sedimentation
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ORIGINAL ARTICLE
Evolutionary optimization of neural network to predict sediment transport without sedimentation Isa Ebtehaj1 · Hossein Bonakdari1
· Amir Hossein Zaji1 · Bahram Gharabaghi2
Received: 21 July 2017 / Accepted: 28 September 2020 © The Author(s) 2020
Abstract Sedimentation in open channels occurs frequently and is relative to system inflow. The long-term retention of sediments on channel beds can increase the possibility of variations in deposits and their eventual consolidation. This study compares three hybrid artificial intelligence methods in estimating sediment transport without sedimentation (STWS). We employed the Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) methods in combination with the Artificial Neural Network (ANN) to overcome the weakness of ANN training with conventional algorithms. We used the ICA, GA and PSO methods to optimize the weights of the ANN layers. Using dimensional analysis, we placed the effective parameters in predicting sediment transport into five non-dimensional groups. Six models are proposed and run using three hybrid methods (18 models in total). As the comparisons demonstrate, the proposed combined models are more accurate than ANN and existing equations in estimating the densimetric Froude number (Fr). However, we found the ICA–ANN superior to GA–ANN and PSO–ANN, as it produces explicit solutions to the problem. The ICA–ANN has the lowest prediction uncertainty band for Fr of all developed models. Moreover, the variation trend of the Fr for all input variables (except overall friction factor of sediment) is a second-order polynomial. Keywords Bed load · Combined model · Limit of deposition · Optimization · Sediment transport · Sensitivity analysis
Introduction Water flowing through open channels often contains sediments. If the channel’s transport capacity is insufficient to transport sediment, solids will deposit. Sediment retention on a riverbed without movement for long periods rises the risk of alteration and the ultimate cementation. During low flow in particular, the permanent deposition on channel beds alters the velocity and the shear stress distribution. Channel pipes are designed based on the concept of self-cleaning. Accordingly, the velocity of the flow passing through a channel must, therefore, be capable of washing the deposited sediments away. Consequently, channel design based on self-cleansing should be done in such manner as to meet the following conditions: first, the channel’s equal or over-the-limit-flow must
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Hossein Bonakdari [email protected]
1
Department of Civil Engineering, Razi University, Kermanshah, Iran
2
School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada
have the capacity to transport the minimum concentration of small, suspended particles or low-mass particles. Second, the bed load’s flow capacity for transporting rough particles must be at a level that limits the depth of deposition up to a specific pipe diameter. Among the simplest ways to
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