Slope stability prediction model based on multi-strategy improved SSA for optimizing KELM

doi:10.16265/j.cnki.issn1003-3033.2025.03.0134

Abstract

Abstract:

In order to predict the slope state more accurately and effectively prevent the slope instability accident, an improved ISSA-KELM slope stability prediction model was proposed. Firstly, six main factors such as bulk density and cohesion in slope instability characteristics were used as prediction indexes to establish a data set for slope stability evaluation. Secondly, SSA was enhanced by incorporating Sine chaotic mapping, Levy flight strategy, dynamic adaptive weights, and fusion of optimal explosion strategy and reverse learning. These improvements aimed at enhancing the global search capability and stability of SSA. Subsequently, ISSA was employed to optimize the kernel parameter ψ and regularization coefficient C in KELM for improved prediction accuracy while avoiding overfitting issues associated with KELM. The results show that the accuracy rate, precision, recall rate and F₁ score of ISSA-KELM model reached 0.945 9, 1, 0.866 7 and 0.929, respectively, which are superior to SSA-KELM, PSO-KELM and PSO-SVM models, and the predicted results of the model are the closest to the actual values. It shows that the established ISSA-KELM model has strong generalization ability.

Key words: slope stability, prediction model, improved sparrow search algorithm (ISSA), kernel extreme learning machine (KELM), prediction index, confusion matrix

CLC Number:

X936

QI Yun, XUE Kailong, LI Xuping, WANG Wei, BAI Chenhao, JI Zhunze. Slope stability prediction model based on multi-strategy improved SSA for optimizing KELM[J]. China Safety Science Journal, 2025, 35(3): 92-98.

Figures/Tables 10

Table 1

Standard test function

测试函数	维度	取值范围
$f 1 = - ∑ i = 1 n (x i s i n (\| x i \|))$	30	[-500, 500]
$f 2 = ∑ i = 1 n [x i 2 - 10 c o s (2 π x i + 10)]$	30	[-5.12, 5.12]
$f 3 = - ∑ i = 1 n [(x - a i) (x - a i) T + c i] - 1$	30	[0, 10]
$f 4 = ∑ i = 1 n a i - x 1 (b i 2 + b i x 2) b i 2 + b i x 3 + x 4$	30	[-5, 5]

Table 1

Fig.1

Table 2

Fig.2

Table 3

Fig.3

Fig.4

Table 4

Table 5

Fig.5

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预测结果	实际结果
预测结果	破坏	稳定
破坏	TP	FP
稳定	FN	TN

编号	γ/ (kN·m^-3)	c /kPa	φ/ (°)	β/ (°)	H /m	r_u/ kPa
1	17.93	78.20	18.49	33.42	120.79	0.00
2	18.02	40.92	21.18	21.86	21.86	0.10
︙	︙	︙	︙	︙	︙	︙
149	21.03	0.00	17.72	5.79	57.31	0.00
150	25.74	0.00	17.23	30.03	80.53	0.40
151	22.40	10.00	35.00	45.00	10.00	0.40
152	19.63	11.98	20.00	22.00	12.19	0.25
︙	︙	︙	︙	︙	︙	︙
256	18.84	14.36	25.00	20.00	30.50	0.45
257	19.97	19.96	36.00	45.50	50.00	0.50

模型	模型性能
模型	A_c	P	R	F₁
ISSA-KELM	0.980 8	1	0.966 7	0.983
SSA-KELM	0.942 3	0.965 5	0.933 3	0.949
PSO-KELM	0.884 6	0.928 6	0.866 7	0.897
PSO-SVM	0.826 9	0.920 0	0. 766 7	0.836

模型	模型性能
模型	A_c	P	R	F₁
ISSA-AKELM	0.945 9	1	0.866 7	0.929
SSA-AKELM	0.916 7	0.923 0	0.857 1	0.889
PSO-AKELM	0.864 9	0.857 1	0.800 0	0.828
PSO-ASVM	0.810 8	0.785 7	0.733 3	0.828

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