Prediction of the Flow Regime Index for Dual‐Triangular Opposed‐Flow Drip Emitters Based on Particle Swarm Optimization and Interpretability Analysis

The flow regime index characterizes the sensitivity of emitter discharge to pressure variation. This study developed an interpretable machine‐learning framework to predict the flow regime index. Support vector regression (SVR), backpropagation (BP) neural networks and extreme learning machine (ELM) models were compared. The kernel and activation functions were screened using fivefold cross‐validation, and particle swarm optimization (PSO) was used to optimize the model hyperparameters. Performance was evaluated using R 2 , RMSE, MAE, MBE and CI. The results indicate that PSO‐BP 1 showed the best overall performance. A representative PSO‐BP 1 model selected according to the internal‐validation results achieved R 2 , RMSE, MAE, MBE and CI values of 0.900, 0.0010, 0.00080, 2.0 × 10 −5 and 1.6 × 10 −12 , respectively. In external validation, PSO‐BP 1 and PSO‐BP 2 both achieved an RMSE of 0.0010 and an MAE of 0.00070, providing preliminary evidence of predictive performance within the investigated parameter range. SHAP analysis identified the spacing between water‐retaining components ( f ) as the most influential input, followed by the vertex angle of the water‐retaining component ( θ 2 ); the upper‐wall spacing ( c ) and flow‐splitting‐component vertex angle ( θ 1 ) contributed less. Strong interactions occurred between θ 1 and θ 2 and between c and θ 2 . The framework supports rapid emitter screening and quantifies model‐based contributions of geometric parameters.