Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

Abstract

A b s t r a c t. The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters.K e y w o r d s: cation exchange capacity, fractal theory, particle size distribution, pedotransfer functions

Performance of Soil Cation Exchange Capacity Pedotransfer Function as Affected by the Inputs and Database Size

“…Several PTFs have been derived to estimate CEC from basic soil attributes. [1,9,[13][14][15][16][17][18][19][20][21] However, PTFs developed for one region may not give adequate estimates for other regions. [22] Furthermore, some of these PTFs (and most of them in arid/semiarid soils and in countries with insufficient soil taxonomic data) have been developed based on a limited number of data points.…”

Determination of a suitable model for prediction of soil cation exchange capacity

“…In recent years, various PTFs have been developed to estimate CEC from basic physical and chemical soil properties (McBratney et al 2002;Amini et al 2005;Kianpoor et al 2012;Bayat et al 2014;Liao et al 2014). In most of these models, CEC is assumed to be a linear function of soil organic carbon and clay content (McBratney et al 2002;Sarmadian and Taghizadeh Mehrjardi (2008); Kianpoor et al 2012).…”

“…A recent approach to model PTFs is the use of artificial neural networks (ANNs). Artificial neural networks have been successfully employed to predict some soil properties that their measurement is difficult (Minasny and McBratney 2002;Amini et al 2005;Bayat et al 2014;Emamgolizadeh et al 2015). An advantage of using ANNs is that no specific type of function needs to be assumed a priori to model the relationship between inputs and outputs.…”

“…The optimum relation that links input data to output data is obtained through a training procedure. ANN Models are generally expected to be superior to MLR models because of their greater feasibility (Amini et al 2005;Bayat et al 2014;Emamgolizadeh et al 2015). A type of artificial neural network known as multi-layer perceptron (MLP), which uses a back-propagation training algorithm, is usually used for generating PTFs (Minasny and McBratney 2002;Amini et al 2005;Sarmadian and Taghizadeh Mehrjardi (2008); Lake et al 2009;Keshavarzia and Sarmadiana 2010; Yilmaz and Kaynar 2011;Kianpoor et al 2012;Emamgolizadeh et al 2015).…”

“…Several attempts have been conducted in relation to modeling various soil physiochemical parameters by means of different artificial intelligence-based model techniques such as those done for modeling of the daily and hourly behavior of runoff (Aqil et al 2007), estimation of soil erosion and nutrient concentrations in runoff (Kim and Gilley 2008), modeling of Pb(II) adsorption from aqueous solution (Yetilmezsoy and Demirel 2008), to determine the clay dispersibility (Zorluer et al 2010), estimating the grout ability of granular soils (Tekin and Akbas 2011), prediction of swell potential of clayey soils (Yilmaz and Kaynar 2011), prediction of soil water retention curve (Abbasi et al 2011), land suitability evaluation (Keshavarzi et al 2011), estimating wet soil aggregate stability (Besalatpour et al 2013) and etc. Some studies also have been considered capability of soft computing techniques for prediction modeling soil CEC such as those conducted by Amini et al (2005); Sarmadian and Taghizadeh Mehrjardi (2008); Tang et al (2009); Sarmadian et al (2013); Kianpoor et al (2012), Keshavarzi et al (2012), Liao et al (2014), Bayat et al (2014), Emamgolizadeh et al (2015); Zolfaghari et al (2016). The findings of these researchers demonstrated that PTFs developed through artificial intelligence-based modeling techniques were more efficient than the regression ones to predict the CEC.…”