Volume 9, Issue 2 (4-2019)                   2019, 9(2): 331-342 | Back to browse issues page

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Yosefvand F, Shabanlou S, Kardar S. ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM OPTIMIZATION USING PSO FOR PREDICTING SEDIMENT TRANSPORT IN SEWERS. International Journal of Optimization in Civil Engineering 2019; 9 (2) :331-342
URL: http://ijoce.iust.ac.ir/article-1-393-en.html
Abstract:   (13862 Views)
The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Particle Swarm Optimization (PSO) algorithm a hybrid algorithm (ANFIS-PSO) is developed for predicting the Froude number of three phase flows. This inference system is a set of if-then rules which is able to approximate non-linear functions. In this model, PSO is employed for increasing the ANFIS efficiency by adjusting membership functions as well as minimizing error values. In fact, the PSO algorithm is considered as an evolutionary computational method for optimizing the process continues and discontinues decision making functions. Additionally, PSO is considered as a population-based search method where each potential solution, known as a swarm, represents a particle of a population. In this approach, the particle position is changed continuously in a multidimensional search space, until reaching the optimal response and or computational limitations. At first, 127 ANFIS-PSO models are defined using parameters affecting the Froude number. Then, by analyzing the ANFIS-PSO model results, the superior model is presented. For the superior model, the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and the determination coefficient (R2) were calculated equal to 5.929, 0.324 and 0.975, respectively.
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Type of Study: Research | Subject: Applications
Received: 2018/12/17 | Accepted: 2018/12/17 | Published: 2018/12/17

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