An Estimation of the Discharge Exponent of a Drip Irrigation Emitter by Response Surface Methodology and Machine Learning

• Main Authors: Chen, X. (Author), He, K. (Author), Wei, Z. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: artificial neural network; drip irrigation; error correction; estimation method; hydraulic performance; Hydraulic performance; instrumentation; Irrigation; labyrinth channel emitter; Labyrinth channel emitter; Labyrinth channels; machine learning; Machine learning; Mean absolute error; Mean square error; Neural networks; parameter estimation; response surface methodology; Response-surface methodology; Root mean square errors; Structural parameter; support vector machine; support vector regression; Support vector regression models; Support vector regressions; Surface properties; Tooth heights
• Online Access: View Fulltext in Publisher

 The discharge exponent is a general index used to evaluate the hydraulic performance of emitters, which is affected by emitters’ structural parameters. Accurately estimating the effect of change in structural parameters on the discharge exponent is critical for the design and optimization of emitters. In this research, the response surface methodology (RSM) and two machine learning models, the artificial neural network (ANN) and support vector regression (SVR), are used to predict the discharge exponent of tooth-shaped labyrinth channel emitters. The input parameters consist of the number of channel units (N), channel depth (D), tooth angle (α), tooth height (H) and channel width (W). The applied models are assessed through the coefficient of determination (R2 ), root-mean-square error (RMSE) and mean absolute error (MAE). The analysis of variance shows that tooth height had the greatest effect on the discharge exponent. Statistical criteria indicate that among the three models, the SVR model has the highest prediction accuracy and the best robustness with an average R2 of 0.9696, an average RMSE of 0.0037 and an average MAE of 0.0031. The SVR model can quickly and accurately simulate the discharge exponent of emitters, which is conducive to the rapid design of the emitter. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.