Modelling the unsaturated hydraulic conductivity of a sandy loam soil using Gaussian process regression

Naji Mordi N Al-Dosary,Mohammed A Al-Sulaiman,Abdulwahed M Aboukarima

doi:10.4314/wsa.v45i1.14

Abstract

Unsaturated soil hydraulic conductivity is a main parameter in agricultural and environmental studies, necessary for predicting and managing water and solute transport in soils. This parameter is difficult to measure in agricultural fields; thus, a simple and practical estimation method would be preferable, and quantitative methods (analytical and numerical) to predict the field parameters should be developed. Field experiments were conducted to collect water quality data to model the unsaturated hydraulic conductivity of a sandy loam soil. A mini disk infiltrometer (MDI) was used to measure soil infiltration rate. Input variables included electrical conductivity and the sodium adsorption ratio of irrigation water. Suction rate (pressure head), soil bulk density, and soil moisture content acted as inputs, with unsaturated soil hydraulic conductivity as output. The performance of Gaussian process regression (GPR) was analysed, with multiple linear regression (LR) and multi-layer perceptron (MLP) models used for comparison. Three performance criteria were compared: correlation coefficient (r), root mean square error (RMSE), and mean absolute error (MAE). The simulations employed the Waikato environment for knowledge analysis (WEKA) open source tool. The results indicate that the GPR with Pearson VII function-based universal kernel (PUK kernel), cache size 250007, Omega 1.0 and Sigma 1.0 performs better than other kernels when evaluating test split data, with a correlation coefficient of 0.9646. The RMSEs for GPR (PUK kernel), MLP, and LR were 1.16 × 10−04, 1.87 × 10−04, and 2.22 × 10−04 cm·s−1, respectively. Predictive data mining algorithms (DMA) enable an estimate of unknown values based on patterns in a database. Therefore, the present methodology can be put to use in predictive tools to manage water and solute transport in soils, as the GPR model provides much greater accuracy than the LR and MLP models in predicting the unsaturated hydraulic conductivity of a sandy loam soil.

Highlights

Water management is vital to improve the efficiency and sustainability of agricultural systems, as water is scarce in semi-arid regions such as Saudi Arabia
The study results showed that an artificial neural network model could accurately estimate the unsaturated hydraulic conductivity, and silt, clay, sand, bulk density, and soil organic matter were the most influential input variables
Criteria for evaluating the accuracy of the selected water, SARw ((meq·L−1)1/2) is sodium adsorption ratio of predictive models irrigation water calculated based on the concentrations of Na, Ca, and Mg expressed in milli-equivalents per litre, Experimentally, this study evaluated and compared the MC is soil moisture content (% db), BD is soil bulk density prediction accuracy of the selected predictive models based on (g·cm−3) and SR is suction rate

Summary

INTRODUCTION

Water management is vital to improve the efficiency and sustainability of agricultural systems, as water is scarce in semi-arid regions such as Saudi Arabia. To predict the unsaturated hydraulic conductivity of soil, Moosavi and Sepaskhah (2012b) used an artificial neural network model with input parameters of sand, silt, clay, bulk density, soil organic matter, and initial and saturated volumetric water content. The study results showed that an artificial neural network model could accurately estimate the unsaturated hydraulic conductivity, and silt, clay, sand, bulk density, and soil organic matter were the most influential input variables. In this study, field experiments using different water qualities were conducted to collect data that represent the unsaturated hydraulic conductivity of sandy loam soil This field data was used for modelling the unsaturated hydraulic conductivity of the soil based on water and soil properties (i.e., electrical conductivity and the sodium-adsorption ratio of the irrigation water, soil moisture content, soil bulk density, and suction rate). A multiple linear regression (LR) and a multi-layer perceptron (MLP) model were used as baseline for comparison with the Gaussian process regression (GPR) model

MATERIALS AND METHODS

RESULTS

DISCUSSION

Findings

CONCLUSIONS