Estimation of wetting patterns for surface drip irrigation using moment analysis and interpretable PSO-SVM-AdaBoost model

A thorough understanding of soil wetting patterns during infiltration is essential for designing surface drip irrigation systems and placing soil moisture sensors. This study systematically evaluated variations in centroid depth ( z c ), horizontal ( σ x ) and vertical ( σ z ) standard deviations across nine soil textures, three discharge rates (1, 2, and 3 L·h −1 ), and three initial soil water contents (30 %, 50 %, 70 % of maximum available water) using Hydrus-2D/3D numerical simulations combined with spatial moment analysis. Based on these results, a machine learning model combining particle swarm optimization (PSO), support vector machine (SVM), and adaptive boosting (AdaBoost) was developed and compared with multiple linear regression (MLR), SVM, and PSO-SVM models. Soil texture and initial water content had greater influence on z c , σ x , and σ z than discharge rates. The PSO-SVM-AdaBoost model achieved the highest accuracy, with Bias , Root Mean Square Error ( RMSE ), and the Coefficient of Determination ( R 2 ) for the test set of −0.129 cm, 1.139 cm, and 0.989 for z c ; −0.034 cm, 0.366 cm, and 0.996 for σ x ; and −0.169 cm, 1.426 cm, and 0.984 for σ z . Furthermore, to address concerns regarding the “black-box” nature of the model, the explainable artificial intelligence (XAI) framework SHapley Additive exPlanations (SHAP) was applied, revealing that cumulative infiltration flux (Q 3D ) contributed most significantly to z c , σ x , and σ z , while discharge rates contributed the least. The PSO-SVM-AdaBoost model and its interpretability framework proposed in this study provide technical support for the design of surface drip irrigation systems and the optimal placement of soil moisture sensors.