Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China)

Huang, Faming; Yin, Kunlong; He, Tao; Zhou, Chao; Zhang, Jun

doi:10.17559/tv-20150314105216

Cited by 10 publications

(8 citation statements)

References 52 publications

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“…Twenty base estimators were used for bagging LR with a regularization strength of 10. Multi-layer perceptron (MLP) with five hidden layers (32,24,16,8, and 16 neurons) and relu activation units was trained with Adam optimizer. The ANN used in this study had 5 of these MLPs as a base estimator for bagging.…”

Section: Pixel-basedmentioning

confidence: 99%

“…There is an ongoing effort towards developing an automated algorithm for mapping of landslides as well. The majority of the work done so far prefers supervised learning approaches, with an assumption that landslides are more likely to occur under conditions similar to those that have caused the past events [12,24,25]. Landslide information of a region, compiled in the past trough manual operation of specialists, can be used to learn patterns from EO data which will further help in automatic identification of landslides in areas which are not yet mapped.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

2020

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Mapping landslides using automated methods is a challenging task, which is still largely done using human efforts. Today, the availability of high-resolution EO data products is increasing exponentially, and one of the targets is to exploit this data source for the rapid generation of landslide inventory. Conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. In addition, recent advances in CNN (convolutional neural network), a type of deep-learning method, has been widely successful in extracting information from images and have outperformed other conventional learning methods. In the last few years, there have been only a few attempts to adapt CNN for landslide mapping. In this study, we introduce a modified U-Net model for semantic segmentation of landslides at a regional scale from EO data using ResNet34 blocks for feature extraction. We also compare this with conventional pixel-based and object-based methods. The experiment was done in Douglas County, a study area selected in the south of Portland in Oregon, USA, and landslide inventory extracted from SLIDO (Statewide Landslide Information Database of Oregon) was considered as the ground truth. Landslide mapping is an imbalanced learning problem with very limited availability of training data. Our network was trained on a combination of focal Tversky loss and cross-entropy loss functions using augmented image tiles sampled from a selected training area. The deep-learning method was observed to have a better performance than the conventional methods with an MCC (Matthews correlation coefficient) score of 0.495 and a POD (probability of detection) rate of 0.72 .

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Section: Pixel-basedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

2020

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“…The modeling processes of data-based models are simpler and more accurate than those of physically-based models [12]. Nevertheless, accurate prediction of the deformation behavior of slopes remains a challenge [13,14].…”

Section: Displacement Prediction Of Natural and Human-induced Slopesmentioning

confidence: 99%

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

Ma¹,

Mei²,

Zhang³

et al. 2020

Preprint

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Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of single monitoring points based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system, and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

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“…The slope deformation prediction models mainly include two categories: physicallybased models and data-based models [27]. The modeling processes of data-based models are simpler and more accurate than those of physically-based models [28]. Nevertheless, accurate prediction of the deformation behavior of slopes remains a challenge [9,11].…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data

Mei

Prezioso

et al. 2021

Neural Comput & Applic

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Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of a single monitoring point based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

show abstract

Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China)

Cited by 10 publications

References 52 publications

Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data

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