## 1 Introduction
Although the traditional empirical formula can be used to build the correlation model, the adjustment coefficient of the formula is difficult to determine [15; 21], which dramatically reduces its prediction accuracy. In recent years, owing to the universal application of artificial intelligence (AI) approaches [22; 23; 24; 25], machine learning (ML) has attracted increasing interest among soil scientists, and as a method for predicting soil properties [26; 27; 28]. ML is an interdisciplinary subfield of AI that promotes low-cost computing through algorithmic learning [29]. ML methods do not rely on a long list of physics-based equations and the prediction accuracy depends on the size of the dataset and the type of algorithm. ML can be divided into the following categories based on the application requirements: regression, probability estimation, classification, and clustering [30]. Regression is mainly utilized for predicting material properties. Commonly used regression algorithms in ML include the backpropagation neural network (BPNN), partial least squares regression (PLSR), and support vector regression (SVR) [31]. The BPNN model is the most widely used form of neural network and has strong nonlinear mapping capabilities. The PLSR model is particularly useful for predicting a group of dependent variables from a large number of independent variables, especially when there is a linear correlation between variables. The SVR model is based on support vector machine (SVM) theory, which can effectively simplify the complexity of high-dimensional space. Because of their advantages, BPNN, PLSR, and SVR models are widely used to predict soil properties. For example, [PERSON] et al., compared the prediction performance of PLSR and BPNN for heavy metal concentrations in soil, and the results showed that the prediction accuracy of the BPNN model was higher than that of the PLSR model [32]. [PERSON] et al., applied SVR to predict the soil organic matter content and achieved good prediction results [33]. [PERSON] et al., used clay content, natural moisture content, liquid limit, plastic limit, specific gravity, and void ratio as input variables to predict soil internal friction angle using the BPNN model [34]. This paper proposes a new method that predicts the shear strength parameters of agricultural soils using a combination of CPT data and soil properties. A portable CPT data measuring device that can quickly measure cone tip resistance and cone side pressure through one penetration operation was designed. Given that water content and bulk density have a clear influence on soil shear strength [2; 17] and that they are easily obtained using the oven-drying method [35], these two soil properties were combined with the CPT data points for constructing the prediction models. To our knowledge, there are no studies that have explored predicting the soil shear strength parameters by combining CPT data and soil properties. As mentioned above, ML allows for low-cost computing through algorithmic learning, and the BPNN, PLSR, and SVR models are the most commonly used regression algorithms. In this study, ML models based on the BPNN, PLSR and SVR algorithms have been used to predict the soil shear strength parameters. Generally, each model has its own limitations [36]. For the BPNN model, there is no exact calculation formula for the number of hidden layer neurons (_h_). Selecting the penalty factor (_p_) and kernel parameter (_g_) for SVR modeling is very difficult. The PLSR model is not effective for nonlinear data and its modeling accuracy is affected by the number of principal component factors (PCFs). To address these challenges, a trial-and-error method is usually used to determine the h of the BPNN model, the grid search method is used to determine the values of \(p\) and \(g\) in the SVR model, and a leave-one-out cross-validation method is used to determine the optimal number of PCFs used in the PLSR model [33; 37; 38; 39].