# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 1 Introduction
[25] used the Support Vector Machine (SVM), Additive Regression Support Vector Machine (AD-SVM), and Imperialist Competitive Algorithm Support Vector Machine (ICA-SVM) to predict the compaction parameters of lateritic soils, and the comparison showed that the ICA-SVM algorithm outperformed the other two algorithms. [PERSON] et al. [16] used Artificial Neural Network (ANN) to develop a predictive model for compaction parameters and permeability of soils. [PERSON] et al. [12] used Artificial Neural Network (ANN), Least Squares Support Vector Machine (LS-SVM), and Multiple Adaptive Regression Spline Curve (MARS) to develop a prediction model for the compaction parameters of sandy soils, and the MARS model was more accurate. [PERSON] [26] constructed a prediction model for aggregate base materials by ANN trained with different numbers of hidden layers, different numbers of hidden layer neurons, and three activation functions. The results showed that the hyperbolic tangent function (Tanh) is the most effective activation function and the performance of the ANN deteriorates with an increase in the number of hidden layers or the number of neurons per hidden layer. [PERSON] et al. [27] used Gene Expression Programming (GEP) and Multiple Gene Expression Programming (MEP) to develop a prediction model for the compaction parameters of expansive soils. In addition, the researchers conducted feature importance analysis, which showed that liquid limit (LL), plastic limit (PL), and sand content (S) affect OMC and MDD predictions [12; 28]. Machine learning (ML) is an important branch of artificial intelligence. In this paper, ML is used to predict OMC and MDD. Compared to traditional methods, ML saves a lot of time and labor cost through the process of data processing, model training, and model testing, and it can achieve more accurate results. Compared to multivariate linear regression, ML can handle large, nonlinear data and is able to continuously learn and improve as it receives new data to adapt to the needs of engineering and the evolution of the data. For a trained machine learning model, it has the adaptability and generalization ability to handle various types of data and tasks, and it still performs well in the face of never-before-seen data. These advantages make machine learning an important tool and technique in various fields. However, there are fewer studies using machine learning (especially Random Forest and Extreme Gradient Boosting Tree) to predict MDD and OMC. Therefore, further research on prediction models for MDD and OMC based on machine learning is necessary. Firstly, this paper builds MDD and OMC prediction models based on 168 sets of soil sample data collected by using four machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting Tree (XGBoost), and Artificial Neural Network (ANN). Secondly, in order to improve the performance and generalization ability of the models, hyperparameter tuning and K-fold cross-validation are carried out in this paper. Then, the best models among the four algorithms are compared to obtain the most effective and accurate final model for predicting OMC and MDD by evaluating the metrics. Finally, feature importance analysis is used to investigate the impact of each feature on OMC and MDD to improve the interpretability of the overall model.