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ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Hydrosphere
Volume 12 - 2024 |
doi: 10.3389/feart.2024.1455124
Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model
Provisionally accepted
Xiaosong Zhu
1*
Xiaolong Pei
2*
Siqi Yang
2,3
Wei Wang
1*
Yue Dong
1*
Mengyang Fang
2,3
Wenjie Liu
3,4
Lingxiu Jiang
2,3*- 1 Langfang Natural Resources Comprehensive Survey Center, China Geological Survey, Langfang, China
- 2 Haikou Marine Geological Survey Center, China Geological Survey, Haikou, Hainan Province, China
- 3 Other, Sanya, China
- 4 School of Ecology, Hainan University, Haikou, China
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. 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. 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 1589, 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.Moreover, further attention is required on the characteristics of sample data and environmental variables in machine learning-based predictions.
Keywords: ground substrate, machine learning, parameter optimization, model validation, Thickness prediction
Received: 27 Jun 2024; Accepted: 03 Sep 2024.
Copyright: © 2024 Zhu, Pei, Yang, Wang, Dong, Fang, Liu and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaosong Zhu, Langfang Natural Resources Comprehensive Survey Center, China Geological Survey, Langfang, China
Xiaolong Pei, Haikou Marine Geological Survey Center, China Geological Survey, Haikou, 571127, Hainan Province, China
Wei Wang, Langfang Natural Resources Comprehensive Survey Center, China Geological Survey, Langfang, China
Yue Dong, Langfang Natural Resources Comprehensive Survey Center, China Geological Survey, Langfang, China
Lingxiu Jiang, Haikou Marine Geological Survey Center, China Geological Survey, Haikou, 571127, Hainan Province, China
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