AU - Asadi, M. J.
AU - Shabanlou, S.
AU - Najarchi, M.
AU - Najafizadeh, M. M.
TI - Application of Evolutionary Algorithm to Optimization of ANNIS Model for Discharge Coefficient Circular Side Spillway Modeling
PT - JOURNAL ARTICLE
TA - JSTNAR
JN - JSTNAR
VO - 23
VI - 3
IP - 3
4099 - http://jstnar.iut.ac.ir/article-1-3708-en.html
4100 - http://jstnar.iut.ac.ir/article-1-3708-en.pdf
SO - JSTNAR 3
ABĀ - In this study, the discharge coefficient of the circular side orifices was predicted using a new hybrid method. Combinations made in this study were divided into two sections: 1) the combination of two algorithms including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and providing the PSOGA algorithm 2) using the PSOGA algorithm in order to optimize the Adaptive Neuro Fuzzy Inference Systems (ANFIS) network and providing the ANFIS-PSOGA method. Next, by identifying the parameters affecting on the discharge coefficient of the circular side orifices, 11 different combinations were provided. Then, the sensitivity analysis conducted by ANFIS showed that the Froude number and the ratio of the flow depth to the orifice diameter (Ym/D) were identified as the most effective parameters in modeling the discharge coefficient. Also, the best combination including the Froude number (Fr), the ratio of the main channel width to the side orifice diameter (B/D), the ratio of the orifice crest height to its diameter (W/D) and the ratio of the flow depth to the orifice diameter (Ym/D) for estimating the discharge coefficient was introduced. For this model, the values of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and correlation coefficient (R) were obtained 0.021, 0.020 and 0.871, respectively. Additionally, the performance of the ANFIS-PSOGA method was compared with the ANFIS-PSO and ANFIS methods. The results showed that the ANFIS-PSOGA method for predicting the discharge coefficient was the superior model
CP - IRAN
IN - 1. Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran.
LG - eng
PB - JSTNAR
PG - 183
PT - Research
YR - 2019