The deformation prediction of mine slope surface using PSO-SVM model
Based on the main factors with important influence on thedeformation of the mine slope, a new methodintegrating support vector machine (SVM) and particleswarm optimization (PSO) was proposed to predict thedeformation of mine slope surface. Themeteorological factors and the deformation data of the re...
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| Main Authors: | , , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2013-12-01.
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| LEADER | 02270 am a22003373u 4500 | ||
|---|---|---|---|
| 001 | ijeecs2891_4019 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Du, Sunwen |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Zhang, Jin |e author |
| 700 | 1 | 0 | |a Li, Jingtao |e author |
| 700 | 1 | 0 | |a Su, Qiaomei |e author |
| 700 | 1 | 0 | |a Zhu, Wenbo |e author |
| 700 | 1 | 0 | |a Chen, Yuejuan |e author |
| 245 | 0 | 0 | |a The deformation prediction of mine slope surface using PSO-SVM model |
| 260 | |b Institute of Advanced Engineering and Science, |c 2013-12-01. | ||
| 520 | |a Based on the main factors with important influence on thedeformation of the mine slope, a new methodintegrating support vector machine (SVM) and particleswarm optimization (PSO) was proposed to predict thedeformation of mine slope surface. Themeteorological factors and the deformation data of the research area are acquired using the advanced deformation monitoring equipment GroundBased-Synthetic Aperture Radar (GB-SAR).Then the SVM is used to predict the mine slope deformation. The PSO is employed to optimize the structure parameters of the SVM. The proposed newmethod was applied to predict the mine slope surface deformation of theAnjialing diggings in China. The obtained experiments results indicated thatthe proposed method can provide precise prediction of the mining slope surfacedeformation and its performance is superior to its rivals. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3732 | ||
| 540 | |a Copyright (c) 2013 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Engineering | ||
| 690 | |a Geologic measurements, meteorological factors, forecasting, particle swarm optimization, support vector machine | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 11, No 12: December 2013; 7182-7189 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v11.i12 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/2891/4019 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/2891/4019 |z Get fulltext |