[1] |
GE Pan-meng, CHEN Bo, CHEN Wei-nan, ZHU Ming-yuan.
An OWOA-RFWSVR-DLM-based Model for Predicting Dam Deformation in the Alpine Region
[J]. Journal of Yangtze River Scientific Research Institute, 2023, 40(5): 153-159.
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[2] |
CHEN Guang-yao, WANG Ming-wu, JIN Ju-liang.
CMFOA-SVM Model for Evaluating Slope Stability
[J]. Journal of Yangtze River Scientific Research Institute, 2023, 40(2): 95-101.
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[3] |
LIU Ying, ZHENG Rong-wei, QI Yan-fang, CHEN Hong-cai.
Optimizing Design of Pipe Network in Micro-irrigation System Using SVMs-GA
[J]. Journal of Yangtze River Scientific Research Institute, 2022, 39(5): 71-75.
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[4] |
HU Jiang, WANG Chun-hong, MA Fu-heng.
Selecting Temperature Factor for Deformation Prediction Model for Super-high Arch Dams During Initial Operation
[J]. Journal of Yangtze River Scientific Research Institute, 2021, 38(1): 59-65.
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[5] |
WU Kai,YANG Xue-lian,LI Jia.
Intelligent Optimization of Supporting Parameters for Large Underground Caverns Based on DE-LSSVM
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2019, 36(9): 115-120.
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[6] |
LIU Chong, SHEN Zhen-zhong, GAN Lei, DANZENG Chi-lie, YAN Zhong-qi.
A Time Series Prediction Model of High Slope Displacement Based on Support Vector Machine and Elman Neural Network
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2019, 36(5): 62-68.
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[7] |
LI Shi-bo, LI De-ying, ZHANG Yu-en, LI Jie.
Displacement Prediction of Baishuihe Step-like Landslide by Least Square Support Vector Machine
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2019, 36(4): 55-59,76.
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[8] |
CAO Yan-ming, JING De-quan, LIU Chun-gao.
Predicting Arch Dam Displacement Using Twin Support Vector Machine Optimized by Artificial Immune Algorithm
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2019, 36(12): 54-58.
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[9] |
QIAN Qiu-pei, CUI Wei-jie, BAO Teng-fei, LI Hui.
Case Analysis of the Prediction Ability of SVM-based Monitoring Model for Concrete Dam Deformation
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2018, 35(8): 46-50.
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[10] |
ZHANG Bi.
Application of Optimized Support Vector Machines and V/S Analysis to Tunnel Deformation Prediction and Trend Judgment
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2018, 35(4): 67-71.
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[11] |
ZHOU Yong-sheng.
Application of Multi-stage Progressive Model to Predicting Foundation Pit Deformation
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2017, 34(8): 47-51.
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[12] |
AN Kai-qiang, NIU Rui-qing.
Landslide Susceptibility Assessment Using Support Vector Machine Based on Weighted-information Model
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2016, 33(8): 47-51.
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[13] |
HUANG Wei-jie,WU Ye,CHEN Zhi-jian,YU Jun-ping.
Prediction on Axial Force of Pile Group in BridgeFoundation Based on ACO-SVM
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2016, 33(1): 121-125.
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[14] |
JIANG Zhen-xiang, XU Zhen-kai, WEI Bo-wen.
A Monitoring Model of Dam Displacement Based onWavelet Decomposition and Support Vector Machine
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2016, 33(1): 43-47.
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[15] |
YU Jun-ping, CHEN Zhi-jian, WU Li-jun, YU Shi-yuan, WANG Shu.
Forecasting Slope Displacement Based on Support Vector Machine Optimized by Ant Colony Algorithm
[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2015, 32(4): 22-27.
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