| Autor: |
Sunwen Du, Guorui Feng, Jianmin Wang, Shizhe Feng, Reza Malekian, Zhixiong Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Energies, Vol 12, Iss 7, p 1288 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en12071288 |
| Popis: |
Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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