New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity

Samad Emamgholizadeh,Babak Mohammadi

doi:10.1007/s00500-021-06095-4

Abstract

Soil cation exchange capacity (CEC) strongly influences the chemical, physical, and biological properties of soil. As the direct measurement of the CEC is difficult, costly, and time-consuming, the indirect estimation of CEC from chemical and physical parameters has been considered as an alternative method by researchers. Accordingly, in this study, a new hybrid model using a support vector machine (SVM), coupling with particle swarm optimization (PSO), and integrated invasive weed optimization (IWO) algorithm is developed for estimating the soil CEC. The physical and chemical data (i.e., clay, organic matter (OM), and pH) from two field sites of Taybad and Semnan in Iran were used for validating the new proposed approach. The ability of the proposed model (SVM-PSOIWO) was compared with the individual model (SVM) and the hybrid model (SVM-PSO). The results of the SVM-PSOIWO model were also compared with those of existing studies. Different performance evaluation criteria such as RMSE, R2, MAE, RRMSE, and MAPE, Box plots, and scatter diagrams were used to test the ability of the proposed models for estimation of the CEC values. The results showed that the SVM-PSOIWO model with the RMSE (R2) of 0.229 Cmol + kg−1 (0.924) was better than those of the SVM and SVM-PSO models with the RMSE (R2) of 0.335 Cmol + kg−1 (0.843) and 0.279 Cmol + kg−1 (0.888), respectively. Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models. The results indicated that the SVM-PSOIWO model estimates the CEC more accurately than existing studies.

Highlights

The soil cation exchange capacity (CEC) is the total number of exchangeable cations that held in the soil by electrostatic forces at a specified pH in the unit weight (Amini et al 2005; Velde and Bauer 2014)
The main goal of this study is to examine the ability of the support vector machines (SVM)-PSOIWO method to estimate CEC
To examine the ability of models to predict the CEC, the whole data sets consisted of 500 experimental data points of organic matter (OM), pH, clay, and soil cation exchange capacity (CEC) split into two categories of training and testing based on simple random sampling approach

Summary

Introduction

The soil cation exchange capacity (CEC) is the total number of exchangeable cations that held in the soil by electrostatic forces at a specified pH in the unit weight (Amini et al 2005; Velde and Bauer 2014). Several studies, such as Emamgholizadeh (2012); Parhizkar et al, (2015); Emamgholizadeh et al, (2017); Parsaie et al, (2018a, b); Maroufpoor et al, (2018); Emamgholizadeh et al (2018); Emamgholizadeh, and Karimi (2019); Bazoobandi et al, (2019); Parsaie et al, (2018a,b), have reported successful applications of these intelligent models to estimate parameters in soil science, water engineering, and civil engineering, for modeling soil CEC in a nonlinear framework and create relationships between inputs (physicochemical properties of soil data) and output (CEC) (da Silva et al 2018; Emamgolizadeh et al 2015; Ghorbani et al 2015; Jafarzadeh et al 2016; Kashi et al 2014; Keshavarzi and Sarmadian 2010; Keshavarzi et al 2017; Liao et al 2014). The accuracy of these models to retrieve the CEC was better than regression-based PTFs models when the relationship between input and output data is unknown, and there is a nonlinear and complex relationship between them (Emamgolizadeh et al 2015)

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Conclusion