Evaluation of side orifices shape factor using the novel approach self-adaptive extreme learning machine

Abstract

Generally, side orifices are used to regulate and measure the flow within open channels. In this paper, discharge coefficient of side orifices was modeled using the novel method “Self-Adaptive Extreme Learning Machine” for the first time. In current study, ability of the numerical model was evaluated using Monte Carlo simulations. Additionally, the validation of the soft computing model was done using the k-fold cross validation approach. The value of k was considered equal to 5. Then, effective parameters on discharge coefficient were identified. Additionally, the number of the hidden layer neurons was selected equal to 30. Also, the sigmoid activation function was chosen as the most optimum activation function. Using input parameters, five different self-adaptive extreme learning machine (SAELM) models were developed. Additionally, the shape factor of the side orifice was studied. The results of numerical models showed that models with shape factor simulated discharge coefficient with higher accuracy. After that, the superior model (SAELM 1) was introduced and R, RMSE, and MAPE for this model were computed 0.995, 0.004, and 0.092. Moreover, the MAE for the superior model was calculated 0.0004. The superior model predicted the discharge coefficient using all input parameters. The results of the sensitivity analysis showed that the ratio of flow depth in main channel to side orifice length Ym/D was identified as the most effective input parameter.