Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model

Introduction The thickness of ground substrate in shallow mountainous areas is a crucial indicator for substrate investigations and a key factor in evaluating substrate quality and function. Reliable data acquisition methods are essential for effective investigation. Methods This study utilizes six machine learning algorithms—Gradient Boosting Machine (GB), Random Forest (RF), AdaBoost Regressor (AB), Neural Network (NN), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN)—to predict ground substrate thickness. Grid search optimization was employed to fine-tune model parameters. The models’ performances were evaluated using four metrics: mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R 2 ). The optimal parameter combinations for each model were then used to calculate the spatial distribution of ground substrate thickness in the study area. Results The results indicate that after parameter optimization, all models showed significant reductions in the MSE, RMSE, and MAE, while R 2 values increased substantially. Under optimal parameters, the RF model achieved an MSE of 1,589, RMSE of 39.8, MAE of 26.5, and an R 2 of 0.63, with a Pearson correlation coefficient of 0.80, outperforming the other models. Therefore, parameter tuning is a necessary step in using machine learning models to predict ground substrate thickness, and the performance of all six models improved significantly after tuning. Overall, ensemble learning models provided better predictive performance than other machine learning models, with the RF model demonstrating the best accuracy and robustness. Discussion Moreover, further attention is required on the characteristics of sample data and environmental variables in machine learning-based predictions.