Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements

Deng, Lizheng; Smith, Alister; Dixon, Neil; Yuan, Hongyong

doi:10.1016/j.enggeo.2021.106315

Cited by 44 publications

(11 citation statements)

References 51 publications

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“…Generally, the dynamic movement of a landslide is subject to internal geological conditions and external triggering factors [4,21]. As for landslides on the reservoir bank of TGR, the fluctuation of the reservoir water level and varying precipitation are two main external factors influencing landslide behaviours [22,23].…”

Section: Attribute Augmentation By Incorporating External Factorsmentioning

confidence: 99%

“…Based on the problems mentioned above and inspired by current encouraging results in traffic forecasting problems, there is a need to combine GCN and RNN models to build a collaborative prediction model to capture spatial and temporal features for spatial-temporal forecast problems. However, displacement prediction of a landslide relies not only on historical GNSS measurements and the spatial correlations of the monitoring network but also on internal geological conditions and various external factors, such as hydrologic conditions [19][20][21][22], anthropogenic factors, etc. For example, in China's Three Gorges Reservoir area, many landslides are triggered and accelerated by seasonal precipitation and the fluctuation of reservoir water level [17,19]; thus, the impact factors in predicting landslide deformation are indispensable during modelling.…”

Section: Introductionmentioning

confidence: 99%

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A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

Jiang

Luo

et al. 2022

Remote Sensing

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Landslide displacement prediction is crucial for the early warning of slope failure but remains a challenging task due to its spatiotemporal complexity. Although temporal dependency has been well studied and discussed, spatial dependence is relatively less explored due to its significant variations of the spatial structure of landslides. In this study, a novel graph convolutional incorporating GRU network (GC-GRU-N) is proposed and applied to landslide displacement forecasts. The model conducts attribute-augmented graph convolution (GC) operations on GNSS displacement data with weighted adjacency matrices and an attribute-augmented unit to combine features, including the displacements, the distance, and other external influence factors to capture spatial dependence. The output of multi-weight graph convolution is then applied to the gated recurrent unit (GRU) network to learn temporal dependencies. The related optimal hyper-parameters are determined by comparison experiments. When applied to two typical landslide sites in the Three Gorge Reservoir (TGR), China, GC-GRU-N outperformed the comparative models in both cases. The ablation experiment results also show that the attribute augmentation, which considers external factors of landslide displacement, can further improve the model’s prediction performance. We conclude that the GC-GRU-N model can provide robust landslide displacement forecasting with high efficiency.

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Section: Attribute Augmentation By Incorporating External Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

Jiang

Luo

et al. 2022

Remote Sensing

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“…In the literature, several solutions are available to perform variable selection, including stepwise procedures (Atkinson and Massari, 1998;Beguería, 2006;Meusburger and Alewell, 2009), LASSO (Castro Camilo et al, 2017;Amato et al, 2019;Deng et al, 2021) or pe-the best GAM model selection. Stepwise forward selection (SFS) is an iterative approach that aims at identifying the optimal set of variables that strikes a balance between performance and simplicity, reducing overfitting and improving the generalizability of the model Khan et al (2007).…”

Section: Stepwise Gammentioning

confidence: 99%

Assessing multi-hazard susceptibility to cryospheric hazards: lesson learnt from an Alaskan example

Elia¹,

Castellaro²,

Dahal³

et al. 2023

Preprint

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Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions, primarily because of the limited presence of human settlements and, therefore, the little need for risk assessment. However, global warming is radically changing this situation and will change it even more in the future. For this reason, understanding the spatial and spatiotemporal dynamics of geomorphological processes in peri-arctic environments can be crucial to make informed decisions in such unstable environments and shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of data-driven models capable of recognizing locations prone to develop retrogressive thaw slumps (RTSs) and/or active layer detachments (ALDs). These are cryospheric hazards induced by permafrost degradation, and their development can negatively affect human settlements or infrastructure, change the sediment budget dynamics and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate the probability of RST and ALD occurrences in the North sector of the Alaskan territory. The results we obtain show that our binary classifiers can accurately recognize locations prone to RTS and ALD, in a number of goodness-of-fit (AUC_RTS = 0.83; AUC_ALD = 0.86), random cross-validation (mean AUC_RTS = 0.82; mean AUC_ALD = 0.86), and spatial cross-validation (mean AUC_RTS = 0.74; mean AUC_ALD = 0.80) routines.Overall, our analytical protocol has been implemented to build an open-source tool scripted in Python as part of an interactive Jupyter notebook where all the operational steps are automatized for anyone to replicate the same experiment. Our protocol allows one to access cloud-stored information, pre-process it, and download it locally to be integrated for spatial predictive purposes. Data and codes can be accessed at this GitHub repository: https://github.com/zincoblenda/CryoS

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“…Taking into consideration the lag fluctuation of the groundwater level, the SVC-PSO-SVR model was proposed to predict landslide displacement by Han et al [33]. Deng et al [34] used acoustic emission sound generation and rainfall as data inputs, and the equivalent reservoir water level function model was combined with Lasso-ELM to improve the accuracy of landslide displacement prediction. Based on the traditional gray prediction model, L et al improved and proposed a new gray prediction model [13].…”

Section: Introductionmentioning

confidence: 99%

Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model

Lin

Liang

et al. 2022

Mathematics

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In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement.

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Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements

Cited by 44 publications

References 51 publications

A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

Assessing multi-hazard susceptibility to cryospheric hazards: lesson learnt from an Alaskan example

Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model

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