The application of DInSAR and Bayesian statistics for the assessment of landslide susceptibility

Kouhartsiouk, Dimitris; Perdikou, Skevi

doi:10.1007/s11069-020-04433-7

Cited by 18 publications

(7 citation statements)

References 44 publications

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“…When larger spatial units are used, such as the basin ( [64]), there are problems of generalization of the value of the causative factors that sometimes hinder the statistical relationship between cause and effect. The use of the slope as the basic unit of analysis also avoids other extrapolation problems associated with MAUP ([65]), which have been addressed by authors such as [66]; [67], [68] using different strategies.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Susceptibility to Slope Movements in the Environment of Road Infrastructures. Models for Obtaining Predictive Mapping Applied to Roads in the Provinces of Malaga and Granada (Spain)

Perles-Roselló¹,

García²,

Sortino-Barrionuevo³

et al. 2021

Preprint

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This study addresses the peculiarities of the generation of slope movements in the context of road infrastructures and provide a predictive mapping of susceptibility to movements on slopes adjacent to road infrastructures (rockfalls). An inventory of slopes movements was mapped. From the development of the inventory of constant cases of mobilization that is used as a dependent variable, two statistical models can be obtained and compared for the same study area. One of them is based on the concept of frequency, whilst the other one is based on the application of a logistic regression. The results reveal the preponderant importance of lithology as a predictive variable, followed, at a considerable distance, by the slope gradient. Likewise, the importance of an unnatural and characteristic variable area of study, such as the presence of artificial cuts, is indicated as a causative factor. The results show a high degree of coincidence between the tendency of susceptibility predicted by the model, and the effective presence of empirical mobilization signs on the slopes, with Area Under Curve (AUC) values for Receiver Operating Characteristics (ROC) around 0.8.

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Section: Resultsmentioning

confidence: 99%

Analysis of Susceptibility to Slope Movements in the Environment of Road Infrastructures. Models for Obtaining Predictive Mapping Applied to Roads in the Provinces of Malaga and Granada (Spain)

Perles-Roselló¹,

García²,

Sortino-Barrionuevo³

et al. 2021

Preprint

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“…The overwhelming majority of researches on LSE are in terms of known landslides. Moreover, a handful of studies carried out LSE according to the potential landslides, instead of the historical ones (Nhu et al, 2020;Kouhartsiouk and Perdikou, 2021). These above works involving surface deformation detection have greatly improved the accuracy of LSE.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Wang

et al. 2023

Front. Earth Sci.

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Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.

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“…At present, the LSP models coupled with various connection methods mainly include analytic hierarchy process (AHP) model (Ma et al, 2021), information value (IV) model (Zhao B. et al, 2021), mathematical statistics (Kouhartsiouk and Perdikou 2021) and machine learning models, where the machine learning models applied to LSP usually refer to C5.0 Decision Tree (C5.0 DT) (Mao et al, 2017), logistic regression (Shahabi et al, 2015), artificial neural network (Huang et al, 2019), extreme learning machine (Huang et al, 2017a), support vector machine (SVM) (Marjanović et al, 2011), gray correlation degree (Yu et al, 2021), random forest (Sun et al, 2020), clustering algorithm (Guo Z. et al, 2021), semi-supervised multilayer perceptron model (Huang et al, 2020b), etc. In general, the machine learning models are considered to have higher LSP performance than those of the heuristic models and conventional statistical models, due to their efficient nonlinear prediction abilities of machine learning models (Xiao et al, 2021;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model

et al. 2022

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This paper aims to propose an efficient landslide susceptibility prediction (LSP) model based on the frequency ratio method and C5.0 Decision Tree (C5.0 DT) model. Taking Ruijin City as the study area, local landslide inventory and 12 environmental factors are collected. Then the nonlinear correlations between landslide inventory and environmental factors are established by frequency ratio (FR) method. Thirdly, the FR values of these environmental factors are taken as the input variables of the C5.0 DT/SVM models; landslide samples and non-landslide samples are set as the output variables with values of 1 and 0, respectively. The mathematical relationship between input variables and output variables is established by C5.0 DT/SVM models. Finally, the performance of LSP of both models is evaluated by the Area Under Receiver Operation Characteristic Curve (AUC). Results show that: 1) The landslide susceptibility mapping (LSM) of the C5.0 DT and the SVM models are similar on the whole, high and very high susceptibility levels are mainly distributed in the north and in the edge of the study area. 2) The AUC values of C5.0 DT and SVM are 0.886 and 0.819, respectively. Both models have good LSP accuracy, however, the overall LSP accuracy of the C5.0 DT model is better than that of SVM. 3) It is significant and reliable to carry out LSP based on frequency ratio method and C5.0 DT model.

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The application of DInSAR and Bayesian statistics for the assessment of landslide susceptibility

Cited by 18 publications

References 44 publications

Analysis of Susceptibility to Slope Movements in the Environment of Road Infrastructures. Models for Obtaining Predictive Mapping Applied to Roads in the Provinces of Malaga and Granada (Spain)

Analysis of Susceptibility to Slope Movements in the Environment of Road Infrastructures. Models for Obtaining Predictive Mapping Applied to Roads in the Provinces of Malaga and Granada (Spain)

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model

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