The site‐specific selection of the infiltration model based on the global dataset and random forest algorithm

Abstract

AbstractThe selection of the infiltration equation is important in various water research and management fields. Different infiltration equations were found to perform better than others in previous studies. Our objective was to use the Soil Water Infiltration Global (SWIG) database for (a) evaluating and comparing infiltration equations and (b) using the random forest algorithm to investigate how soil properties, land use, and infiltration measurement methods influence the infiltration equation selection. The performance of six equations (Horton, Mezencev, Collis‐George, Green–Ampt, Philip, and Swartzendruber) was characterized by the Akaike information criterion obtained after fitting them to 4,830 cumulative infiltration datasets from the SWIG database. Then, the random forest algorithm was applied to predict the best infiltration equation using soil texture class, organic matter content, bulk density, saturated hydraulic conductivity, land use, and infiltration measurement method as inputs. The Horton, Mezencev, and Collis‐George models were the best in 36, 24, and 12% of cases, respectively. Swartzendruber, Philip, and Green–Ampt were the best in 11, 10, and 7% cases, respectively. The different error of predicting the best infiltration equation was observed in different parts of input variable space. The infiltration method was by far the most important predictor for a model being the best across the whole database, followed by the organic C content and land use type. Organic C content and land use type were the most important predictors for the tension infiltrometer datasets. The SWIG database presents the opportunity to forecast which infiltration equation will work best in site‐specific conditions.