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

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.

Applications of deep learning for phishing detection: a systematic literature review

“…Recently, hybrid machine learning models using optimization algorithms have been proposed and applied in different problems [ 47 ], Emamgholizadeh and Mohammadi [ 22 ]. This kind of hybrid models can also be built using deep learning algorithms to develop highly accurate phishing detection models in future work.…”

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

“…First, a limited initial population is randomly generated and scattered in the problemsolving space. When determining the amount of initial population and reproduction, in IWO optimization method, every member of the population produces seeds according to its capabilities [50]. The product varies linearly from the smallest possible number of seeds to the largest number, and the weeds produce more seeds with better adaptation so that the mean is zero and the standard deviation varies at different stages, ensuring that the randomly distributed seeds are very close to their parent plant.…”

“…This approach allows the models reach to their maximum capabilities, and then the new hybrid model can have advantages of both the ANFIS and optimization algorithms for estimation [48]. Previous studies have proven that such coupled optimized techniques can provide better results in hydrological modeling [50][51][52]. Table 3 provides the optimal parameters related to the machine learning models used.…”

Data driven of underground water level using artificial intelligence hybrid algorithms

“…Emamgholizadeh and Mohammadi presented a new hybrid method based on SVM, PSO, and IWO models with SVM-PSOIWO structure for estimating soil exchange capability (CEC). Based on the findings of this paper, it can be concluded that the novel combination algorithm, when applied to the prediction of a three-month period with RMSE (R2) of 0.229 Cmol + kg −1 (0.924), has a high degree of accuracy 13 . Vadiati et al, by FL, ANFIS and SVM predicted the underground water level in the Tehran Karaj plain.…”