Prediction study on slope stability based on LightGBM algorithm

doi:10.16265/j.cnki.issn1003-3033.2022.07.1473

Abstract

Abstract:

In order to reduce the disasters and accidents caused by the slope instability, a slope stability prediction model based on the LightGBM algorithm was proposed. Six main influencing factors, i.e. volume weight, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio, were taken as the input of the model, and the slope stability was taken as the output of the model. The classification performance metrics based on the confusion matrix and the area under curve (AUC) of the test performance curve were introduced to evaluate the generalization performance of this model. The results show that the proposed prediction model can well describe the complex nonlinear relationship between the influencing factors and the slope stability. Compared with other algorithms, the F₁-Score and AUC of the LightGBM algorithm are 0.92 and 0.91, respectively, increasing by 1.10%-61.40% and 1.11%-28.17%, respectively. Compared with zero-mean normalization and arctangent normalization, the normalization of the minimum-maximum values considering the positive and negative correlations is more applicable to the data preprocessing of LightGBM model. Furthermore, by changing the length of the training dataset and single factor analysis, it is found that the generalization performance of the model is positively correlated with the length of the training dataset.

Key words: light gradient boosting machine(LightGBM), slope stability, machine learning algorithm, confusion matrix, normalization

ZHANG Kai, ZHANG Ke. Prediction study on slope stability based on LightGBM algorithm[J]. China Safety Science Journal, 2022, 32(7): 113-120.

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预测结果	真实值
预测结果	稳定	失稳
稳定	TP	FP
失稳	FN	TN

序号	γ/ (kN·m^-3)	c/ kPa	φ/ (°)	Φ/ (°)	H/ m	r_u	S
1	17.93	78.20	18.49	33.42	120.79	0.0	0
2	18.02	40.92	21.18	21.86	34.56	0.1	1
3	25.76	64.11	21.40	15.76	30.38	0.5	1
︙	︙	︙	︙	︙	︙	︙	︙
149	21.03	0.00	17.72	5.79	57.31	0.0	1
150	25.74	0.00	17.23	30.03	80.53	0.4	0

序号	γ/ (kN·m^-3)	c/ kPa	φ/ (°)	Φ/ (°)	H/ m	r_u	S
1	18.80	14.40	25.02	19.98	30.06	0.0	1
2	18.77	30.01	9.99	25.02	50.00	0.1	1
3	19.97	19.96	36.00	45.50	50.00	0.5	0
︙	︙	︙	︙	︙	︙	︙	︙
106	26.70	50.00	26.60	50.00	170.00	0.25	1
107	26.80	60.00	28.80	59.00	108.00	0.25	1

机器学习算法	F₁-Score	AUC	备注
支持向量机分类	0.79	0.82	文献[11]
K最近邻	0.57	0.78
决策树	0.57	0.73
极限梯度提升算法	0.76	0.77
随机森林	0.63	0.79
线性判别分析	0.76	0.80
多层感知人工神经网络	0.77	0.83
逻辑回归	0.80	0.83
极限随机树	0.71	0.74
高斯朴素贝叶斯	0.72	0.73
装袋算法	0.58	0.71
混合叠加集成算法	0.91	0.90
LightGBM算法	0.92	0.91	文中算法

方法编号	A	B	R	F₁-Score
1	0.92	0.94	0.90	0.92
2	0.83	0.87	0.80	0.83
3	0.78	0.81	0.75	0.78