Аңдатпа
This article presents a hybrid machine learning model designed for soil type classification based on the analysis of geophysical characteristics. The proposed model combines two algorithms – RandomForestClassifier and MLPClassifier – integrating the high accuracy of ensemble methods with the ability of neural networks to capture complex nonlinear dependencies between parameters. The input dataset included indicators such as electrical conductivity, density, P-wave propagation velocity, and burial depth. Prior to training, data preprocessing was performed, including outlier removal, standardization, and categorical feature encoding. The hybrid architecture allowed the integration of results from both models with different weights, optimizing classification accuracy. The effectiveness of the proposed approach was compared with alternative algorithms such as XGBoost and Keras using metrics including Accuracy, F1-score, Precision, and Recall. The hybrid model achieved an accuracy of 96.07%, outperforming individual algorithms. Visualization of confusion matrices provided insights into class distribution and model robustness. The results confirm that combining ensemble and neural methods ensures more stable and reliable predictions when working with geophysical data. The developed model can be effectively applied in geotechnical studies, construction, agriculture, and environmental monitoring, enhancing analytical efficiency and reducing the need for costly laboratory testing.