Blind identification of active landslides in urban areas: a new set of comprehensive criteria

Wang, Xianmin; Guo, Haonan; Ding, Ziyang; Wang, Lizhe

doi:10.1007/s11356-022-22418-w

Cited by 6 publications

(6 citation statements)

References 34 publications

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“… The surface deformation of the slope features spatial continuity. The deformation is spatially continuous when a minimum of 2 × 2 adjacent pixels have velocities more than 2σ [ 77 ]. The lithology is characterized by soft rocks or highly weathered and fractured hard rocks.…”

Section: Methodsmentioning

confidence: 99%

“…e.g., PGA, cumulative rainfall, or distance to road or building. The correlation is quantitatively measured by the Pearson correlation coefficient passing a significance test under the significance level of 0.05 [ 77 ].…”

Section: Methodsmentioning

confidence: 99%

“…The surface deformation of the slope features spatial continuity. The deformation is spatially continuous when a minimum of 2 × 2 adjacent pixels have velocities more than 2σ [ 77 ].…”

Section: Methodsmentioning

confidence: 99%

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Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Wang

Zhang

Jia

et al. 2022

IJERPH

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Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Wang

Zhang

Jia

et al. 2022

IJERPH

Self Cite

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“…A slope is confidently moving when its LOS deformation velocity |V LOS | >2σ, and σ is the standard deviation of the SAR LOS deformation velocity and reflects the uncertainty of a velocity value (Solari et al, 2020;Bekaert et al, 2020). Moreover, the deformation of the slope should possess spatial continuity, i.e., at least 2 × 2 adjacent pixels have velocities higher than 2σ (Wang et al, 2022a). (2) Stratum criterion.…”

Section: Identification Criteria Of Potential Landslidesmentioning

confidence: 99%

“…The relation is measured according to the Pearson correlation coefficients passing a significance test with the significance value lower than 0.05 (Wang et al, 2022b). The relation to an earthquake is calculated by the Pearson correlation coefficient between deformation velocity and peak ground acceleration (PGA), the relation to precipitation is in terms of the Pearson correlation coefficient between cumulative displacement and cumulative rainfall, the relation to snowmelt is determined by the Pearson correlation coefficient between deformation velocity and temperature in the ice and snow melting season, and the relation to human engineering activity is according to the Pearson correlation coefficient between area proportion of active deformation and distance to road or building (Wang et al, 2022a;Wang et al, 2022b). In Wanzhou District, there is no obvious relation between landslides and the occurrence of snowmelt or earthquakes during the monitoring period; thus, only the criteria in precipitation and road construction are adopted in Wanzhou District.…”

Section: Identification Criteria Of Potential Landslidesmentioning

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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Inference of creep landslide slip surface by InSAR technology and improved particle swarm optimization

Shen,

Wang,

Dai

et al. 2024

Landslides

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Blind identification of active landslides in urban areas: a new set of comprehensive criteria

Cited by 6 publications

References 34 publications

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

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

Inference of creep landslide slip surface by InSAR technology and improved particle swarm optimization

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