Prediction of the bed expansion and pressure drop in microirrigation media filter backwashing using artificial neural networks and comparison with other machine learning techniques

Abstract

The filtration capacity of media filters, which are widely used in drip irrigation systems to prevent emitter clogging, must be periodically restored by backwashing, which fluidizes the media bed and removes those trapped particles. Bed expansion (BE) and pressure drop (PD) are the key parameters for assessing the hydraulic performance of backwashing, but the available equations and models frequently fall short of their prediction. An experiment with three medium types, four filter underdrain designs, two bed heights and different backwashing superficial velocities as input variables was conducted to measure both BE and PD. A dataset of 705 backwashing runs was obtained and with 80 % of data for training and 20 % for testing, a machine learning-based model that uses Artificial Neural Networks (ANN) to predict both BE and PD was developed and compared with the Ridge, Elastic-net, and Lasso regression models. With coefficients of determination of 0.9932 and 0.9988 for BE and PD, respectively, the results demonstrated that the ANN model not only ranked the importance of the input variables and showed strong agreement with experimental data but also attained superior predictive accuracy regarding the Lasso, Elastic-net, and Ridge models. This study presents a novel and optimized approach for predicting bed expansion and pressure drop, enhancing the reliability of media filter backwashing performance assessments in smart irrigation systems.