Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm

Zhang, Nian; Niu, Mengmeng; Wan, Fei; Lu, Jiale; Wang, Yaoyao; Yan, Xuehui; Zhou, Caifeng

doi:10.3390/app14020867

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…The main classification methods, such as support vector machine, random forest, Kneighbor, etc., are designed based on the maximum classification accuracy, which often leads to the high classification accuracy of majority classes and the low classification accuracy of minority classes, which reduces the overall performance of the classifier [4][5][6]. Therefore, how to effectively improve the classification performance of the classifier when dealing with unbalanced data has become a hot spot in the field of machine learning and data mining [7,8].…”

Section: Introductionmentioning

confidence: 99%

Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

Chen,

Zou,

Liu

et al. 2024

Symmetry

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The problems of imbalanced datasets are generally considered asymmetric issues. In asymmetric problems, artificial intelligence models may exhibit different biases or preferences when dealing with different classes. In the process of addressing class imbalance learning problems, the classification model will pay too much attention to the majority class samples and cannot guarantee the classification performance of the minority class samples, which might be more valuable. By synthesizing the minority class samples and changing the data distribution, unbalanced datasets can be optimized. Traditional oversampling algorithms have problems of blindness and boundary ambiguity when synthesizing new samples. A modified reclassification algorithm based on Gaussian distribution is put forward. First, the minority class samples are reclassified by the KNN algorithm. Then, different synthesis strategies are selected according to the combination of the minority class samples, and the Gaussian distribution is used to replace the uniform random distribution for interpolation operation under certain classification conditions to reduce the possibility of generating noise samples. The experimental results indicate that the proposed oversampling algorithm can achieve a performance improvement of 2∼8% in evaluation metrics, including G-mean, F-measure, and AUC, compared to traditional oversampling algorithms.

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Section: Introductionmentioning

confidence: 99%

Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

Chen,

Zou,

Liu

et al. 2024

Symmetry

View full text Add to dashboard Cite

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Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach

Gebretsadik,

Kumar,

Fissha

et al. 2024

Discov Appl Sci

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The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in open pit mining. While various parameters can affect rock fragmentation, this study considers ten among them (i.e., spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) to train and test the models. However, due to a weak correlation with rock fragmentation, drill diameter, average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features including the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. Machine learning models (random forest regression, support vector regression, and XG boost), as well as a deep learning model (neural network regression), are applied to develop a practical way that can optimize the prediction of fragmentation. This study employs performance measures such as R-squared, RMSE, MSE, MAPE, and MAE. The optimization of the model revealed promising results, indicating that the architecture 5-64-32-16-1 exhibits strong performance. Specifically, the model achieved mean squared error (MSE) values of 41.32 and 28.59 on the training and test datasets, respectively. The R2 value for both training and test is 0.83. RFR is also performing well compared to SVR and XG boost with MSE values of 12.37 and 9.89 on training and testing data, respectively.in both sets, the R2 value is 94%. Based on permutation importance and shapely plot values, it is observed that the powder factor has the highest impact, while the burden has the lowest impact on fragmentation.

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Intelligent prediction model for water inrush risk in RF water-rich tunnel based on AHP improvement

Wan,

Zhang,

Niu

et al. 2024

Preprint

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To prevent large-scale water inrush accidents during the excavation process of the rich water tunnel, a method based on AHP improved RF for intelligent risk prediction of the water-rich tunnel is proposed. By analyzing the influence of geological and hydrological conditions, design factors, and construction factors, 13 factors were selected as evaluation indicators for the risk of water inrush in the water-rich tunnel, including stratum lithology, poor geology, rock inclination, negative topographic area ratio, surrounding rock grade, hydrodynamic zoning, tunnel length, tunnel burial depth, tunnel section area, advanced geological prediction, excavation method, advance support, and monitoring measurement; Through statistical analysis of a large number of accident cases, a dataset of water inrush accidents in the water-rich Tunnel was established and preprocessed. Using the RF model in machine learning, the weights of each indicator in the RF model are calculated through the application and parameter optimization of the dataset. Then, the weights are optimized through AHP and imported into the RF model to obtain the improved RF-AHP model. Compared with the test set prediction results of the RF model's RF-AHP model, the accuracy of the RF-AHP model reaches 98%, which is better than the RF model's 96%. This indicates that the performance of the improved RF model based on AHP has been improved, and it has good performance in predicting the risk of water inrush in the water-rich tunnel, providing a new means for predicting the risk of water inrush in the water-rich tunnel.

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Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm

Cited by 4 publications

References 16 publications

Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach

Intelligent prediction model for water inrush risk in RF water-rich tunnel based on AHP improvement

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