Abstract
Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements of hydraulic conductivity have many difficulties due to spatial variation of the property in the field. Pertaining to this problem, in this study, estimation models have been developed using machine learning methods (M5 tree model and random forest model) in an attempt to estimate the accurate values of unsaturated hydraulic conductivity related to basic soil properties (clay, silt and sand content, bulk density and moisture content). Data set was collected from the experimental measurements of cumulative infiltration using mini disc infiltrometer at the study area (Kurukshetra, India). A multivariate nonlinear regression (MNLR) relationship was derived, and the performance of this model was compared with the machine learning-based models. The evaluation of the results, based on statistical criteria (R2, RMSE, MAE), suggested that random forest regression model is superior in accurate estimations of the unsaturated hydraulic conductivity of field data relative to M5 model tree and MNLR.
Highlights
It is essential to estimate the hydraulic features of the soil because of their considerable role in dam, hydrological cycle, irrigation system, drainage system and groundwater flow-related studies
This study investigates the potential of M5 tree and random forest (RF) regression models
A relationship based on multiple nonlinear regression (MNLR) is developed for the unsaturated hydraulic conductivity of soil considering sand (%), clay (%), silt (%), bulk density and moisture content as input variables, and the developed relationship is compared with the soft computing-based regression models (M5 and RF)
Summary
It is essential to estimate the hydraulic features of the soil because of their considerable role in dam, hydrological cycle, irrigation system, drainage system and groundwater flow-related studies. Keeping in view the importance of M5 tree and random forest regression techniques, the present research deals with the implementation of these techniques in an attempt to relate unsaturated hydraulic conductivity of the field data measured from 20 locations of Kurukshetra district, Haryana, with the soil physical properties. A relationship based on multiple nonlinear regression (MNLR) is developed for the unsaturated hydraulic conductivity of soil considering sand (%), clay (%), silt (%), bulk density and moisture content as input variables, and the developed relationship is compared with the soft computing-based regression models (M5 and RF). Clay, silt, bulk density and moisture content, and output parameter is unsaturated hydraulic conductivity ( K ) of soil Three standard statistical measures: coefficient of determination ( R2 ), root mean square error ( RMSE ) and mean
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