Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

Abstract

The saturated hydraulic conductivity (K s ) of the soil is one of the main soil physical properties. Indirect estimation of this parameter using pedo-transfer functions (PTFs) has received considerable attention. The Purpose of this study was to improve the estimation of K s using fractal parameters of particle and micro-aggregate size distributions in smectitic soils. In this study 260 disturbed and undisturbed soil samples were collected from Guilan province, the north of Iran. The fractal model of Bird and Perrier was used to compute the fractal parameters of particle and micro-aggregate size distributions. The PTFs were developed by artificial neural networks (ANNs) ensemble to estimate K s by using available soil data and fractal parameters. There were found significant correlations between K s and fractal parameters of particles and microaggregates. Estimation of K s was improved significantly by using fractal parameters of soil micro-aggregates as predictors. But using geometric mean and geometric standard deviation of particles diameter did not improve K s estimations significantly. Using fractal parameters of particles and micro-aggregates simultaneously, had the most effect in the estimation of K s . Generally, fractal parameters can be successfully used as input parameters to improve the estimation of K s in the PTFs in smectitic soils. As a result, ANNs ensemble successfully correlated the fractal parameters of particles and micro-aggregates to K s .

Assessment of infiltration models developed using soft computing techniques

“…A lot of research has been conducted in the field as well as in the laboratory pertaining to the estimation of infiltration characteristics, viz., hydraulic conductivity, cumulative infiltration and infiltration rate of soil, recharging rate, and permeability of soil. Most of the modelling studies recognized the application of ANN on the laboratory as well as field data and substantial number of researchers found appreciable results with this technique (Anari et al, 2011; Esmaeelnejad et al, 2015;Schaap & Leij, 1998;Sedaghat et al, 2016;Sihag, 2018Sihag, , 2018Sy, 2006). Observing some recent field-based studies, illustrated in Table 6, RF regression is recognized as superior modeling method in predicting the infiltration rate and hydraulic conductivity of soil in the region of Kurukshetra, India (Kumar & Sihag, 2019;Singh et al, 2017).…”

Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network

“…Secondly, although both ANNs and random forest are applied to explore the nonlinear and complicated relationships, the former ANNs presents higher quality and better performance than random forest in prediction and classification (Were et al, 2015;Raczko and Zagajewski, 2017). Thirdly, ANNs is one of the most commonly used approaches in salinity studies, including soil salinity prediction of crop root zone (Patel et al, 2002), saturated hydraulic conductivity (Motaghian and Mohammadi, 2011;Sedaghat et al, 2016) and soil salinity mapping (Zou et al, 2010;He et al, 2015). Nevertheless, ANNs only consider the variations of the soil properties caused by the correlated environmental factors or the spatial autocorrelation of surrounding measured data (Park and Vlek, 2002;Takata et al, 2007).…”

Estimating soil texture with laser-guided Bouyoucos

“…Soil is an important material from which human beings have benefited since the beginning of the earth. Since the soil is a base component in some areas (agricultural activities for food production, constructional works for building shelters, and many others related to the material science), many issues in natural science such as drainage, water retention capacity, air capacity, erosion susceptibility, organic matter content, cation capacity, pH balance, hydraulic conductivity can be assessed with knowledge of the physical content of the soil [1][2][3][4]. By a simple definition, estimating soil texture is to measure the amount of sand, silt and clay particles [5].…”