GB-SAR Monitoring Effectiveness Analysis Using Genetic Algorithm Optimized BP Neural Network
Abstract According to the characteristic of the GB-SAR affected by many factors and complex relationship in the open pit mine slope ground disaster monitoring, it used genetic algorithm optimized BP neural network model to analyze the monitoring effectiveness of the GB-SAR. The neural network takes the scan gradient, scan slope direction and the radar echo intensity as the input, takes the obtained points number of deformation monitoring as the output, and uses the Pearson correlation coefficient method to analyze the relevant properties and related degree of the influence factors. The results show that the GA-BP algorithm is suitable for monitoring effectiveness analysis of GB-SAR, and it is effective and superior.
Key words :
monitoring effectiveness analysis
GB-SAR
genetic algorithm
BP neural network
Pearson correlation coefficient method
Cite this article:
DU Sunwen,ZHANG Jin,DENG Zengbing et al. GB-SAR Monitoring Effectiveness Analysis Using Genetic Algorithm Optimized BP Neural Network[J]. jgg, 2017, 37(8): 876-880.
DU Sunwen,ZHANG Jin,DENG Zengbing et al. GB-SAR Monitoring Effectiveness Analysis Using Genetic Algorithm Optimized BP Neural Network[J]. jgg, 2017, 37(8): 876-880.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2017/V37/I8/876
[1]
LU Zhaoxing, Lü Zhifeng, LI Ting, ZHANG Jinsheng, YAO Yao. Forecasting of the Variable Geomagnetic Field Based on BP Neural Network [J]. jgg, 2021, 41(3): 229-233.
[2]
HUANG Wenxi, ZHU Fuying, ZHAI Dulin, LIN Jian, QING Yun, LI Xinxing, YANG Jian. Comparative Analysis of BP Neural Network and ARMA Model in Short-Term Prediction of Mid-Latitude TEC [J]. jgg, 2021, 41(3): 262-267.
[3]
CHEN Xingda,YU Xuexiang,CHI Shengsheng,JIANG Chuang,ZHAO Xiangshuo. Surface Subsidence Prediction Model of BP StrongPredictor Fusing Chaos Residuals
[J]. jgg, 2020, 40(9): 913-917.
[4]
XIAO Mengren, CHEN Hao, LUO Li, ZHA Xiaohui, GUO Jiangchun. Study on Inelastic Attenuation and Site Response in Jiangxi Area [J]. jgg, 2020, 40(3): 287-290.
[5]
. [J]. jgg, 2019, 39(增2): 124-126.
[6]
XIE Shaofeng,SU Yongning,LIU Chunli,LIU Lilong. Short-Impending Prediction of GPS Precipitable Water Vapor Based on Wavelet Decomposition and GA-LSSVM [J]. jgg, 2019, 39(5): 487-491.
[7]
ZHOU Yongjiang;YAO Yibin;YAN Xiao;ZHAO Cunjie. Study on Haze Prediction of BP Neural Network Incorporating GNSS Meteorological Parameters [J]. jgg, 2019, 39(11): 1148-1152.
[8]
. [J]. jgg, 2016, 36(增2): 75-.
[9]
. [J]. jgg, 2016, 36(增1): 117-.
[10]
WANG Zhaoling,YANG Qian,LIU Zhengping,SUN Keqin. Genetic Algorithms Inversion of Wave Equation in Tunnel Seismic Prediction [J]. jgg, 2016, 36(5): 451-.
[11]
ZHANG Xiuxia. Inversion of Fault Deformation Parameters Considering Observation Precision [J]. jgg, 2016, 36(11): 977-980.
[12]
SHEN Zhehui,HUANG Teng,SHEN Yueqian,ZHENG Hao. Dam Deformation Monitoring Prediction on Support Vector Machine Optimized by Genetic Algorithm [J]. jgg, 2016, 36(10): 927-930.
[13]
HE Linbang,FENG Jie. The Key Technology of Seabed Sediment Classification
System based on Echo Intensity [J]. jgg, 2015, 35(1): 140-144.
[14]
Hu Jiyuan,Wen Hongyan,Zhou Lü,Chen Guanyu. STUDY ON DAM PREDICTION AND INVERSION WITH MULTI-SOURCE
MONITORING DATA BASED ON IPSO-BP MODEL [J]. jgg, 2014, 34(4): 67-70.
[15]
Zhu Chengguang, Zhou Yong, Li Shipeng,Lin Qiang. RESEARCH OF POINTING ERROR OF SATELLITE LASER RANGING
TELESCOPE [J]. jgg, 2013, 33(Supp.2): 126-128.