Multiple AI model integration strategy—Application to saturated hydraulic conductivity prediction from easily available soil properties

A multiple model integration scheme driven by artificial neural network (ANN) (MM-ANN) was developed and tested to improve the prediction accuracy of soil hydraulic conductivity (Ks) in Tabriz plain, an arid region of Iran. The soil parameters such as silt, clay, organic matter (OM), bulk density (BD), pH and electrical conductivity (EC) were used as model inputs to predict soil Ks. Standalone models including multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), support vector machine (SVM) and extreme learning machine (ELM) were also implemented for comparative evaluation with MM-ANN model predictions. Based on several performance indicators such as Nash Sutcliffe Efficiency (NSE), results showed that the calibrated MM-ANN model involving the predictions of MARS, M5Tree, SVM and ELM models by considering all the soil parameters used in this study as inputs provided superior soil Ks estimates. The proposed hybrid model (MM-ANN) emerged as a reliable intelligence model for the assessment of soil hydraulic conductivity with an NSE = 0.939 & 0.917 during training and testing, respectively. Accurate prediction of field-scale soil hydraulic conductivity is crucial from the view point of agricultural sustainability and management prospects.