Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method

Zhou, Chao; Yin, Kunlong; Cao, Ying; Intrieri, Emanuele; Ahmed, Bayes; Catani, Filippo

doi:10.1007/s10346-018-1022-0

Cited by 146 publications

(89 citation statements)

References 47 publications

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“…It makes the hydrogeological conditions of the reservoir area in Wanzhou represent different characteristics in different periods during the year [44]. [36].…”

Section: Monitoring Data Of Groundwater Levelmentioning

confidence: 99%

“…So, the continuous rise of the groundwater level was affected by the rainfall more than the reservoir level during this period. [36].…”

Section: Monitoring Data Of Groundwater Levelmentioning

confidence: 99%

“…SVM advances a lot in dealing with the problem of nonlinear regression prediction. However, its generalization performance is sensitive to the selection of the parameters [33][34][35][36]. The difficulties in capturing the critical modeling variables are noted as the major drawbacks to SVM [22].…”

Section: Introductionmentioning

confidence: 99%

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Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Cao

Yin

Zhou

et al. 2020

Sensors

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The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.

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“…It makes the hydrogeological conditions of the reservoir area in Wanzhou represent different characteristics in different periods during the year [44]. [36].…”

Section: Monitoring Data Of Groundwater Levelmentioning

confidence: 99%

“…So, the continuous rise of the groundwater level was affected by the rainfall more than the reservoir level during this period. [36].…”

Section: Monitoring Data Of Groundwater Levelmentioning

confidence: 99%

See 1 more Smart Citation

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Cao

Yin

Zhou

et al. 2020

Sensors

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“…Regardless of the method used to build the prediction model, data preprocessing is an essential step in slope displacement prediction, which mainly consists of denoising and normalization [36]. Denoising is used to improve data quality [37,38]. Normalization makes the data dimensionless [39].…”

Section: Data Preprocessingmentioning

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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“…Most of the studies agree on the fact that deep learning convolutional neural networks (CNNs) may be an optimal solution for highly flexible and powerful image classification and object recognition (Shin et al 2016;Du et al 2019). In general, artificial neural networks have long been successfully used to recognize specific landscape characters leading to slope instability (Lee et al 2004;Catani et al 2005;Ermini et al 2005;Pradhan and Lee 2010;Yilmaz 2010;Liu and Wu 2016;Zhou et al 2018a) or to detect anomalous displacements of rock and soil masses (Zhou et al 2018b).…”

Section: Introductionmentioning

confidence: 99%

Landslide detection by deep learning of non-nadiral and crowdsourced optical images

Catani

2020

Landslides

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The recent development of mobile surveying platforms and crowdsourced geoinformation has produced a huge amount of non-validated data that are now available for research and application. In the field of risk analysis, with particular reference to landslide hazard, images generated by autonomous platforms (such as UAVs, ground-based acquisition systems, satellite sensors) and pictures obtained from web data mining are easily gathered and contribute to the fast surge in the amount of nonorganized information that may engulf data storage facilities. Therefore, the high potential impact of such methods is severely reduced by the need of a massive amount of human intelligence tasks (HITs), which is necessary to filter and classify the data, whatever the final purpose. In this work, we present a new set of convolutional neural networks (CNNs) specifically designed for the automated recognition of landslides and mass movements in non-standard pictures that can be used in automated image classification, in supporting UAV autonomous guidance and in the filtering of data-mined information. Computer vision can be of great help in fostering the autonomous capability of intelligent systems to complement, or completely substitute, HITs. Image and object recognition are at the forefront of this research field. The deep learning procedure has been accomplished by applying transfer learning to some of the top-performer CNNs available in the literature. Results show that the deep learning machines, calibrated on a relevant dataset of validated images of landforms, may supply reliable predictions with computational time and resource requirements compatible with most of the UAV platforms and web data mining applications in landslide hazard studies. Average accuracy achieved by the proposed methods ranges between 87 and 90% and is consistently higher than that obtained by generalpurpose state-of-the-art image recognition convolutional neural networks. The method can be applied to early warning, vulnerability assessment, residual risk estimation, model parameterisation and landslide mapping. Specific advantages will be the reduction of the present limitations in the intelligent guidance of landslide mapping drones, the classification of fake news, the validation of post-disaster information and the correct interpretation of an impending change in the environment.

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Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method

Cited by 146 publications

References 47 publications

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

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

Landslide detection by deep learning of non-nadiral and crowdsourced optical images

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