首页|Prediction of landslide displacement with dynamic features using intelligent approaches

Prediction of landslide displacement with dynamic features using intelligent approaches

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Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the pre-diction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorpo-rated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposi-tion with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.

Landslide displacement predictionArtificial intelligent methodsGated recurrent unit neural networkCEEMDANLandslide monitoring

Yonggang Zhang、Jun Tang、Yungming Cheng、Lei Huang、Fei Guo、Xiangjie Yin、Na Li

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Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education,and Department of Geotechnical Engineering,Tongji University,Shanghai 200092,China

College of Civil Engineering,Huaqiao University,Xiamen 316000,China

School of Civil Engineering,Qingdao University of Technology,Qingdao 266000,China

College of Civil Engineering and Architecture,Wenzhou University,Wenzhou 325000,China

Shenzhen Antai Data Monitoring Technology Co.,Ltd.,Shenzhen 518000,China

Key Laboratory of Disaster Prevention and Mitigation of Hubei Province,China Three Gorges University,Yichang 443002,China

School of Resources&Safety Engineering,Central South University,Changsha 410083,China

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Natural Science Foundation of ChinaChina Geological Survey ProjectChina Geological Survey Project

41807294DD201907160001212020CC60002

2022

矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDCSCDSCIEI
影响因子：1.222
ISSN：2095-2686
年,卷(期)：2022.32(3)
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