Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models
Faming Huang1
Haowen Xiong1
Shixuan Chen1
Zhitao Lv1
Jinsong Huang2
Zhilu Chang1
Filippo Catani3
1.School of Civil Engineering and Architecture,Nanchang University,Nanchang 330031,China2.Discipline of Civil,Surveying and Environmental Engineering,Priority Research Centre for Geotechnical Science and Engineering,University of Newcastle,Callaghan,NSW 2287,Australia3.Department of Geosciences,University of Padova,Padua,Italy
摘要：The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.
机标关键词：predictionstabilitymemoryvectormodelslongslopewith
论文发表日期：2023-04-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：14( 83-96 )
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