Machine Learning Approach to Predict Flow Regime Index of a Stellate Water-Retaining Labyrinth Channel Emitter

Abstract

Accurate calculation of the flow regime index in the design and development stage of a drip irrigation emitter plays an essential role. In this study, machine learning technologies were used to establish the relationship between flow channel structural parameters of the novel stellate water-retaining labyrinth channel (SWRLC) irrigation emitter and its flow regime index. The training dataset and test dataset were built by computational fluid dynamics (CFD) simulation and experimental study. The extreme learning machine (ELM), backpropagation neural network (BPNN), and traditional multiple linear regression (MLR) models were developed for the prediction of the flow regime index of the SWRLC emitter. The input parameters matrix consisted of the length of the trapezoid baseline, angle between the hypotenuses of adjacent trapezoids, trapezoid height, radius of stellate water-retaining structure, spacing of two symmetric trapezoids, path depth, and SWRLC unit number, while flow regime index x was the output of the models. The comprehensive indicator (CI) was proposed, and root mean square error (RMSE), mean absolute error (MAE), mean bias error value (MBE), and coefficient of determination (R2) were used to introduce the reliable assessment of the three models. The comparison results showed that the ELM model had the lowest errors, with the CI, RMSE, MAE, and R2 were 1.96 × 10−11, 0.00163, 0.00126, and 91.49%, respectively. The BPNN model had the lowest MBE error with the value of 1.03 × 10−4. The ELM and BPNN models were available and had acceptable accuracy for predicting the flow regime index of the emitter, saving both time and cost and increasing efficiency in the design and development stage. According to the CI, the ELM model performed best, followed by the BPNN model with a minor discrepancy.