Single Valued Neutrosophic Number Ensemble Learning Model for Stability Classification of Open Pit Mine Slopes
China's open pit mining industry faces the dual challenge of increasing production and preventing disasters. In order to ensure the safe exploitation of mineral resources, the stability of slopes must be assessed. In light of the fact that over 1,950 landslide accidents have occurred over the p...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2025-01-01
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| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/EnsembleLearning11.pdf |
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| Summary: | China's open pit mining industry faces the dual challenge of increasing production and preventing disasters. In order to ensure the safe exploitation of mineral resources, the stability of slopes must be assessed. In light of the fact that over 1,950 landslide accidents have occurred over the past decade, accounting for 15% of all safety incidents, the evaluation of slope stability has become a critical research focus in the fields of geo-resources and geo-engineering. Traditional slope stability evaluation methods rely on empirical tools and the expertise of professionals to assess slope stability. In contrast, machine learning (ML) methods offer a more comprehensive approach, analyzing the intricate features present in diverse sampling data. As a novel extension of ML, this paper presents a single-valued neutrosophic number-based ensemble learning (SVNN-EL) model. This model employs binary coding groups (1, 0, 0), (0, 1, 0) and (0, 0, 1) to express the learning outcomes of the slope stability, quasi-stability and instability statuses. Subsequently, a similarity measure is employed to determine the classification results of slopes. Finally, the proposed SVNN-EL model is applied to a case study in Yunnan province, China, the proposed model's four performance metrics, namely accuracy, precision, recall, and F1-score (the harmonic mean of precision and recall), are 0.915, 0.894, 0.948, and 0.921, respectively. A comparison with the k-nearest neighbor, support vector machine and random forest methods reveals that the performance metrics of the proposed SVNN-EL model are superior to those of existing methods. |
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| ISSN: | 2331-6055 2331-608X |