International Journal of Optimization in Civil Engineering
دانشگاه علم و صنعت ایران
Iran University of Science & Technology
http://ijoce.iust.ac.ir
18
agent2
2228-7558
en
jalali
1398
1
1
gregorian
2019
4
1
9
2
online
1
fulltext
en
ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM OPTIMIZATION USING PSO FOR PREDICTING SEDIMENT TRANSPORT IN SEWERS
Applications
Applications
پژوهشي
Research
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 (<em>MAPE</em>), Root Mean Square Error (<em>RMSE</em>) and the determination coefficient (<em>R</em><sup>2</sup>) were calculated equal to 5.929, 0.324 and 0.975, respectively.
Sediments, Circular channel, Hybrid model, ANFIS, Particle Swarm Optimization
331
342
http://ijoce.iust.ac.ir/browse.php?a_code=A-10-66-227&slc_lang=en&sid=1
F.
Yosefvand
`180031947532846001459`

180031947532846001459
Yes
S.
Shabanlou
`180031947532846001460`

180031947532846001460
No
S.
Kardar
`180031947532846001461`

180031947532846001461
No