## 4 Discussion  
More accurate results were obtained from neural networks and support vector machines compared to the result of multiple linear regression in predicting soil compaction and shear stress based on electrical parameters. The regression method generated models of unsatisfying accuracy (R = 0.408 for soil compaction (depth 0.4-0.5 m), R = 0.469 for soil compaction (depth 0-0.5 m), and R = 0.423 for shear stress). Better models were produced with the use of machine learning techniques. The neural networks (MLP and RBF) generated the best models (R > 0.8 for soil compaction and R > 0.65 for shear stress). In the case of shear stress, the model with the lowest accuracy was developed using the SVM method (R = 0.243). Generally, models of higher precision were obtained for soil compaction compared to shear stress. Machine learning techniques were applied in state-of-the-art research for the prediction of various soil parameters. [PERSON] et al. [67] used ANN and SVM to estimate soil penetration resistance based on organic matter content, fertilizing, soil moisture content, and penetration depth. The authors reported that machine learning algorithms outperformed statistical methods. The best SVM model resulted in a correlation coefficient for the test dataset of R = 0.99. The ANN produced slightly worse results (R = 0.91 for the model with 12 neurons in the hidden layer). A support vector regression model was also utilized by [PERSON] et al. [68] when they developed the VisNIR integrated multi-sensing soil penetrometer. The machine learning method was used to calibrate the model for predicting the total carbon and total nitrogen content in soil. The authors reported the high precision of the model (R > 0.9). [PERSON] and [PERSON] [69] employed an ANN technique to develop a prediction model for the normalized cone penetration resistance of silty sands affected by relative density, fine content, and the horizontal coefficient of consolidation. This model was of very high accuracy (R > 0.99 for both the training and testing datasets). [PERSON] et al. [70] compared regression analysis and ANN in modeling the relationships between soil penetration resistance as an output parameter and bulk density and water content as  
Figure 5: The scatter matrix graph for the validation dataset (MLP_SS model). input parameters. The authors reported that the ANN model performed better (R = 0.99) in comparison with regression analysis (R = 0.96). A similar comparison was executed by [PERSON] et al. [71] who used MLR and ANN to estimate bulk density as a function of organic matter content, penetration resistance, and clay and moisture content. The authors concluded that the ANN model provided much better prediction accuracy (R = 0.90 for the test dataset) than the MLR model (R = 0.71 for the test dataset). [PERSON] et al. [72] stated that machine learning algorithms achieved better results than MLR in predicting soil properties in unsampled locations. [PERSON] et al. [73] compared MLR, ANN, and SVM techniques for the prediction of the compaction properties of fine-grained soils based on the various physical properties of soil, and they observed higher accuracy in ANN and SVM. There is a shortage of papers comparing the accuracy of multiple linear regression, artificial neural networks, and support vector machine models for the prediction of soil physical properties. However, these techniques have been used and compared in various fields of agriculture and engineering. Our results are similar to those of [PERSON] et al. [74], who used MLP, ANN, and SVM methods to model above-ground maize biomass using UAV remote sensing data. [PERSON] and [PERSON] [75] used the same techniques to develop sea-level forecasting models and found out that ANN and SVM models performed better than the MLR technique. The model with the best accuracy resulted from the SVM model, with a correlation coefficient of R = 0.709. [PERSON] et al.