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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

Study on SABO-LSTM monitoring model and risk state of slope displacement in water diversion projects

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引水工程边坡位移 SABO-LSTM监测模型及风险状态研究

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Jul. 2026, Vol.49 No.7: 997-1002

DOI: 10.3969/j.issn.1003-5060.2026.07.020

Authors

HUANG Ming, WANG Pengfei

(School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: In this paper, the subtraction-average-based optimizer (SABO) is used to optimize the long short-term memory (LSTM) model, and the SABO-LSTM-based slope displacement monitoring model of water-division project is established, thus achieving the accurate prediction of slope displacement law. The displacement and displacement rate of each measuring point of slope are taken as evaluation indexes. According to the data characteristics of each index, the confidence interval method is used to classify the risk state, and the basic probability assignment (BPA) of each index is calculated according to the interval number distance. Then, the basic probability of each index is fused with the Dempster-Shafer (D-S) evidence theory, and the risk state of slope displacement is evaluated by using the fusion results. Combined with the prediction results of the monitoring model, the pre-evaluation of the risk state of slope displacement is achieved. The research results can provide technical reference for slope displacement prediction and risk state assessment.

Keywords: water diversion project slope; subtraction-average-based optimizer-long short-term memory (SABOLSTM) model; confidence interval method; Dempster-Shafer (D-S) evidence theory; risk state

作者信息

黄铭，王鹏飞

（合肥工业大学土木与水利工程学院，安徽合肥230009）

摘要和关键词

摘要: 文章利用减法平均优化算法（subtraction-average-based optimizer，SABO）对长短期记忆网络（long short-term memory，LSTM）模型进行优化，建立SABO-LSTM引水工程边坡位移监测模型，实现了对边坡位移规律的准确预测。并以边坡各测点位移和位移速率为评估指标，根据各指标数据特征采用置信区间法对其进行风险状态分级，基于区间数距离计算得到各指标的基本概率分配；然后结合D-S（Dempster-Shafer）证据理论对各指标基本概率进行融合，利用融合结果对边坡位移风险状态进行评估，并结合监测模型预测结果，实现了对边坡位移风险状态的预评估。该研究结果可为边坡位移预测及风险状态评估提供技术参考。

关键词: 引水工程边坡；SABO-LSTM模型；置信区间法；D-S证据理论；风险状态

Funding

安徽省自然科学基金资助项目（2208085US01）

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