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Landslide displacement prediction based on the ICEEMDAN,ApEn and the CNN-LSTM models

Journal of Mountain Science 2023年第5期20卷 1220-1231页

作者： LI Li-min WANG Chao-yang WEN Zong-zhou GAO Jian XIA Meng-fan College of Electronics and Information Xi’an Polytechnic UniversityXi’an 710600 China Shaanxi Yixin Network Technology Co. LtdXi’an 710065China

Landslide deformation is affected by its geological conditions and many environmental factors.So it has the characteristics of dynamic,nonlinear and unstable,which makes the prediction of landslide displacement difficult.In view of the above problems,this paper proposes a dynamic prediction model of landslide displacement based on the improvement of complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),approximate entropy(ApEn)and convolution long short-term memory(CNN-LSTM)neural network.Firstly,ICEEMDAN and Ap En are used to decompose the cumulative displacements into trend,periodic and random displacements.Then,the least square quintic polynomial function is used to fit the displacement of trend term,and the CNN-LSTM is used to predict the displacement of periodic term and random term.Finally,the displacement prediction results of trend term,periodic term and random term are superimposed to obtain the cumulative displacement prediction value.The proposed model has been verified in Bazimen landslide in the Three Gorges Reservoir area of China.The experimental results show that the model proposed in this paper can more effectively predict the displacement changes of landslides.As compared with long short-term memory(LSTM)neural network,gated recurrent unit(GRU)network model and back propagation(BP)neural network,CNN-LSTM neural network had higher prediction accuracy in predicting the periodic displacement,with the mean absolute percentage error(MAPE)reduced by 3.621%,6.893% and 15.886% respectively,and the root mean square error(RMSE)reduced by 3.834 mm,3.945 mm and 7.422mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide a new insight for practical landslide prevention and control engineering.

关键词： displacement prediction ICEENDAN Approximate entropy Long short-term memory Bazimen landslide

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Auto machine learning-based modelling and prediction of excavationinduced tunnel displacement

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1100-1114页

作者： Dongmei Zhang Yiming Shen Zhongkai Huang Xiaochuang Xie Key Laboratory of Geotechnical and Underground Engineering Ministry of EducationTongji UniversityShanghai200092China Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai200092China

The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.

关键词： Soilestructure interaction Auto machine learning(AutoML) displacement prediction Robust model Geotechnical engineering

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Adaptive algorithm for estimating excavation-Induced displacements using field performance data

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Underground Space 2020年第2期5卷 115-124页

作者： Haijian Fan Gangqiang Kong Transportation Engineer Texas Department of TransportationUSA College of Civil and Transportation Engineering Hohai UniversityNanjingJiangsu ProvinceChina

Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of the model and(b)the uncertainties of the input variables.The uncertainties of these models can be characterized by a bias factor which is defined as the ratio of the actual displacement to the predicted displacement.The bias factors associated with the C&O method and the KJHH model are evaluated using the Bayesian method and a database of 71 excavations in Shanghai.To improve the predictions of the maximum displacement,an adaptive algorithm is proposed using field performance data.The performance of the proposed algorithm is demonstrated by an example in which excavation-induced displacements are generated by finite element method in normally consolidated clays.The example shows that the developed algorithm can significantly improve the predictions by incorporating the field performance data.

关键词： Excavation displacement prediction Bayesian updating Model bias

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A Predictive,Two-Parameter Model for the Movement of Reservoir Landslides

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Journal of Earth Science 2020年第6期31卷 1051-1057页

作者： Robert E.Criss Wenmin Yao Changdong Li Huiming Tang Department of Earth and Planetary Sciences Washington UniversitySt.Louis MO 63130USA Faculty of Engineering China University of GeosciencesWuhan 430074China

Monitoring data show that many landslides in the Three Gorges region,China,undergo step-like displacements in response to the managed,quasi-sinusoidal annual variations in reservoir level.This behavior is consistent with motion initiating when the reservoir water level falls below a critical level that is intrinsic to each landslide,with the subsequent displacement rate of the landslide being proportional to the water depth below that critical level.Most motion terminates when the water level rises back above the critical level,so the annual step size is the time integral of the instantaneous displacement rate.These responses are incorporated into a differential equation that is easily calibrated with monitoring data,allowing prediction of landslide movement from actual or anticipated reservoir level changes.Model successes include(1)initiation and termination of the annual sliding steps at the critical reservoir level,producing a series of steps;(2)prediction of variable step size,year to year;and(3)approximate prediction of the shape and size of each annual step.Annual rainfall correlates poorly with step size,probably because its effect on groundwater levels is dwarfed by the 30 m annual variations in the level of the Three Gorges Reservoir.Viscous landslide behavior is suggested.

关键词： reservoir landslide parsimonious model step-like displacement displacement prediction critical reservoir level

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Improved Kalman filter method considering multiple factors and its application in landslide prediction

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Frontiers of Earth Science 2020年第3期14卷 625-636页

作者： Qing Ling Wei Qu Qin Zhang Lingjie Kong Jing Zhang Li Zhu College of Geology Engineering and Geomatics Chang’an UniversityXi’an710064China School of Civil Engineering Lanzhou University of TechnologyLanzhou730050China Information Engineering University Zhengzhou450002China

Landslides,seriously threatening human lives and environmental safety,have become some of the most catastrophic natural disasters in hilly and mountainous areas worldwide.Hence,it is necessary to forecast landslide deformation for landslide risk reduction.This paper presents a method of predicting landslide displacement,i.e.,the improved multi-factor Kalman filter(KF)algorithm.The developed model has two advantages over the traditional KF approach.First,it considers multiple external environmental factors(e.g.,rainfall),which are the main triggering factors that may induce slope failure.Second,the model includes random disturbances of triggers.The proposed model was constructed using a time series which consists of over 16-month of data on landslide movement and precipitation collected from the Miaodian loess landslide monitoring system and nearby meteorological stations in Shaanxi province,China.Model validation was performed by predicting movements for periods of up to 7 months in the future.The performance of the developed model was compared with that of the improved single-factor KF,multi-factor KF,multi-factor radial basis function,and multi-factor support vector regression approaches.The results show that the improved multi-factor KF method outperforms the other models and that the predictive capability can be improved by considering random disturbances of triggers.

关键词： landslide improved Kalman filter triggering factors displacement prediction

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