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基于优化极限学习机模型的边坡稳定性预测研究

Study on Slope Stability Prediction Based on Optimized Extreme Learning Machine Model

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边坡稳定性预测对工程安全及地质灾害防治极其重要,目前机器学习在边坡稳定性预测较广泛,例如BP神经网络、支持向量机(SVM)、极限学习机(ELM)等.但传统的ELM模型在预测边坡稳定性时存在易陷入局部最小值、难以选择合适学习率的问题,针对此问题,提出了一种基于主成分分析法(PCA)和爬行动物搜索法(RSA)并行优化极限学习机(ELM)的边坡稳定性预测模型.此模型利用PCA算法对数据进行降维,减少数据的冗余性,并利用RSA算法优化ELM模型的输入层权值和隐含层偏置,极大地提高了模型的预测精度和预测效率.将传统的ELM模型、RSA-ELM模型、PCA-SVM模型及PCA-RSA-ELM 4 种模型进行对比,从而得到PCA-RSA-ELM模型在边坡稳定性预测这类问题上的精确性更高,为边坡稳定性预测分析提供新的思路,对防灾减灾及保护国民经济安全具有重大意义.
Slope stability prediction and analysis are very important for engineering safety and geological disaster preven-tion.At present,machine learning is widely used in slope stability prediction,such as BP neural network,support vector ma-chine(SVM),extreme learning machine(ELM)and so on.However,the traditional ELM model is prone to fall into the local minimum value and is difficult to select the appropriate learning rate when predicting slope stability.Aiming at this problem,a slope stability prediction model based on principal component analysis(PCA)and reptile search method(RSA)parallel opti-mization limit learning machine(ELM)is proposed in this paper.This model uses the PCA algorithm to reduce the dimension of data and reduce the redundancy of data,and uses the RSA algorithm to optimize the input layer weight and hidden layer bias of ELM model,which greatly improves the prediction accuracy and efficiency of the model.By comparing the traditional ELM model,RSA-ELM model,PCA-SVM model and PCA-RSA-ELM model,it's found that the PCA-RSA-ELM model has higher ac-curacy in slope stability prediction,which provides a new idea for slope stability prediction analysis.In the meantime,it is of great significance to disaster prevention and reduction and protection of national economic security.

safety engineeringslope stabilityextreme learning machinePCA dimension reduction algorithmreptile search algorithmconfusion matrix

陈家豪、张燕、杜明芳、黄海荣、徐志军、陈旭

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河南工业大学土木工程学院,河南 郑州 450001

河南省粮油仓储建筑与安全重点实验室,河南 郑州 450001

安全工程 边坡稳定性 极限学习机 PCA降维 爬行动物搜索 混淆矩阵

国家自然科学基金面上项目河南工业大学青年骨干教师培育计划河南省粮油仓储建筑与安全重点实验室开放课题

51978247214201552020KF-B01

2024

10.19614/j.cnki.jsks.202406026

金属矿山
中钢集团马鞍山矿山研究院 中国金属学会

金属矿山

CSTPCD北大核心
影响因子：0.935
ISSN：1001-1250
年,卷(期)：2024.(6)