基于主成分分析和PSO-ELM算法的排土场稳定性预测模型

doi:10.11872/j.issn.1005-2518.2021.05.168

Abstract

Abstract:

Aiming at the stability analysis of dump slope，a PCA-PSO-ELM dump slope stability prediction model is proposed in this paper，which uses principal component analysis method to reduce data redundancy and particle swarm optimization algorithm to optimize the weight threshold of extreme learning machine. Eight prediction indexes of dump stability were determined in this model，including soil cohesion，internal friction angle，dump slope angle，foundation bearing capacity，seismic intensity，rainfall and snowfall conditions，dumping technology and random mining and digging conditions.According to 100 groups of corresponding dump data，training time，RMSE value and determination coefficient R² were used to evaluate and compare the validity of prediction results of PCA-PSO-ELM model，BP neural network model，ELM model and PSO-ELM model.The research results show that as the input variable to train and test the PSO-ELM network model，the dump stability sample data processed by PCA dimensionality reduction，made predicted value very close to the real value.The prediction accuracy and efficiency are not only higher than the ELM algorithm，but also far better than the traditional BP neural network algorithm.Compared with the PSO-ELM model without PCA treatment，the PSO-ELM model optimized by PCA method can significantly shorten the calculation time on the basis of little difference in efficiency，which proves that the method has certain practical value.

Key words: dump sites’ safety, stability evaluation, extreme learning machine, principal component analysis, artificial neutral network, particle swarm algorithm

CLC Number:

Feng GAO,Xiaodong WU,Keping ZHOU. Prediction Model of Soil Dump Stability Based on Principal Component Analysis and PSO-ELM Algorithm[J].Gold Science and Technology, 2021, 29(5): 658-668.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Loading matrix of principal component factor"

影响因素	主成分
影响因素	$X 1$	$X 2$
黏聚力	0.155	0.202
内摩擦角	0.159	0.127
边坡角度	0.126	0.575
地基承载力	0.152	-0.358
地震烈度	0.151	0.390
降雨和降雪条件	0.151	-0.373
排土工艺	0.157	-0.113
乱采乱挖	0.147	-0.367

Table 2

Fig.4

Fig.5

Fig.6

Table 3

Fig.7

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成分	初始特征值			提取平方和载入
成分	合计	方差百分比	累计贡献率/％	合计	方差百分比	累计贡献率/％
1	5.552	69.406	69.406	5.552	69.406	69.406
2	1.049	13.110	82.516	1.049	13.110	82.516
3	0.414	5.179	87.695
4	0.320	3.998	91.693
5	0.220	2.751	94.444
6	0.172	2.151	96.595
7	0.150	1.875	98.470
8	0.122	1.530	100.000

样本编号	真实值	预测值
样本编号	真实值	BPNN	ELM	PSO-ELM	PCA-PSO-ELM
91	5（V）	5.5443	4.6699	5.0936	5.0850
92	3（III）	3.4779	3.1714	3.0214	3.0184
93	1（I）	1.8100	1.1842	0.9521	0.9028
94	4（IV）	4.9611	3.8648	3.8699	3.8195
95	1（I）	1.1654	1.4305	1.0935	1.0040
96	4（IV）	3.6122	3.9996	4.0544	3.9933
97	5（V）	5.6236	4.9438	4.9937	5.0779
98	1（I）	0.8102	0.7896	0.9994	0.9721
99	3（III）	3.9471	3.3157	3.0690	3.0034
100	5（V）	4.9532	4.7209	4.8955	4.9943
RMSE 值时间/s		0.6005	0.2451	0.0747	0.0752
RMSE 值时间/s		0.3533	0.1765	26.9467	12.9354

Prediction Model of Soil Dump Stability Based on Principal Component Analysis and PSO-ELM Algorithm

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