## 1 Introduction
Geophysical results can be used to identify or describe soil properties in different layers. Normally, soil properties and subsequently soil classifications are measured/identified in a laboratory. However, sample preparations and doing the relevant tests in a laboratory are costly and time consuming [10]. For that reason, indirectly classifying soil through the use of geophysics results has received much attention from the academic community. In the geophysics field, the novel computational techniques are faster, easier to carry out, and more precise in comparison to theoretical/empirical techniques [11]. Recently, many researchers in science and engineering fields have made use of soft computing and machine learning (ML) techniques [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. In addition, lots of these techniques have been \begin{table}
\begin{tabular}{c c} \hline \hline
**Area with Casing** & **Area without Casing** \\ \hline \hline Suspension PS * ++ & Suspension PS * \\ Crosshole Seismic * & Crosshole Seismic * \\ \hline \hline \end{tabular}  
* Only when the hole is filled with fluid; ++ PVC casing.  
\end{table} 
Table 1: The preliminary selected list of downhole geophysical methods. adopted by different scholars in geophysics and geotechnics [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Concerning the classification of soil characteristics, several ML techniques were adopted by various studies. The Support Vector Machine (SVM) was the most frequently used technique to classify the soil attributes. Multiple types of SVM including Linear, Gaussian, Polynomial, and Radial Basis were utilized for soil type classification [10; 50; 51; 52; 53; 54; 55; 56]. Furthermore, several types of Decision Trees (DTs), including the Classification and Regression Decision Tree (CART), J48, Random Forest (RF), Bagged trees, and gradient boosting trees were adopted to classify soil characteristics [57; 58; 51; 52]. Some studies also applied the Artificial Neural Network (ANN) and its variants [51; 52] for classifying the soil characteristics. Based on the mentioned studies, it seems that soft computing and ML techniques are able to map a proper, simple, applicable, and accurate way for soil characterizations/classifications. While the studies above signify essential contributions to the application of ML in soil classification research, they also have some significant limitations. These studies deal only with a limited set of ML classifiers, though the number of possible classifiers is large. Advanced DT techniques such as Chi-Square Automatic Interaction Detection (CHAID), Quick, Unbiased, and Efficient Statistical Tree (QUEST), and C5 have not been considered in a comparative study; however, it has been shown that these DTs can generate highly accurate results for many applications. Some variations of ANN, such as the Radial Basis Function (RBF), have been overlooked to classify the soil characteristics. Besides, the probabilistic techniques such as the Bayesian Network (BN) and its variants have not been adopted in soil classification research. This study attempts to employ different types of prediction models, including DT, ANN, and BN, to classify the soil types. To this end, six input variables, including V\({}_{\text{P}}\), V\({}_{\text{s}}\), Moisture content, Liquid Limit (LL), Plastic Limit (PL), and specific gravity of soils (Gs), were taken into consideration. Four DT algorithms, including CART, CHAID, QUEST, and C5; two ANN models, including Multi-Layer Perceptron (MLP) and RBF; and two BN algorithms, including Tree Augmented Naive (TAN) and Markov Blanket were used. The performance of the models employed was assessed through the overall accuracy of each model, the recall and precision of each classifier, and for each soil characteristic and gain. In the following, background of machine learning models together with some descriptions related to case studies, field investigations, and laboratory tests will be given in Section 2.