Rapid prediction of landslide dam stability using the logistic regression method

Shan, Yibo; Chen, Shengshui; Zhong, Qiming

doi:10.1007/s10346-020-01414-6

Cited by 31 publications

(10 citation statements)

References 42 publications

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“…Herein, formed-stable refers to a landslide dam in which the blocked a river/valley still exists or has Frontiers in Earth Science frontiersin.org disappeared due to sediment infilling. Over its lifetime, inflow water may overtopped the dam crest, but neither total failure nor destructive flood occurred (Stefanelli et al, 2015;Stefanelli et al, 2016;Shan et al, 2020). Formed-unstable refers to a landslide dam that had disappeared when statistical work was conducted, and the longevity may have been minutes to centuries after its formation.…”

Section: Dammed Lake Inventories and Terminology Definitionsmentioning

confidence: 99%

“…Formed-unstable refers to a landslide dam that had disappeared when statistical work was conducted, and the longevity may have been minutes to centuries after its formation. A landslide dam can be classified as formed-unstable if human intervention has strongly influenced the dam morphology and hydrological conditions to prevent potential disasters (Stefanelli et al, 2015;Stefanelli et al, 2016;Shan et al, 2020). Unknown means the current status of a landslide dam could not be verified during the investigation.…”

Section: Dammed Lake Inventories and Terminology Definitionsmentioning

confidence: 99%

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Review on risk assessments of dammed lakes

Zhong

Wang²,

Shan³

et al. 2023

Front. Earth Sci.

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As one type of natural disaster, dammed lakes pose a serious threat to the safety of lives and properties downstream. Scientific risk assessments of dammed lakes are key for pre-disaster prevention and post-disaster rescue. However, due to the lack of basic information and uncertainty surrounding materials and loads, risk assessments of dammed lakes are more complex than those of artificial reservoir dams, and comprehensive assessment methods are lacking. Based on the evolution of dammed lake hazard chains, starting with the concept of a dammed lake risk assessment, this paper focused on six aspects: worldwide dammed lake databases, hazard assessments for landslide dams, breach mechanisms and breach processes, flood routing after landslide dam breaching, loss assessments, and risk mitigation measures. A comprehensive review was conducted on the qualitative and quantitative risk assessment methods around the world, as well as future outlooks.

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Section: Dammed Lake Inventories and Terminology Definitionsmentioning

confidence: 99%

Section: Dammed Lake Inventories and Terminology Definitionsmentioning

confidence: 99%

Review on risk assessments of dammed lakes

Zhong

Wang²,

Shan³

et al. 2023

Front. Earth Sci.

Self Cite

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“…In the logistics regression model, if ZB＜ 0.5, the landslide dam is stable; if ZB＞0.5, the landslide dam is formed-unstable. Shan et al (2020) established a database for rapid prediction of dam stability. In the database, 50 documented historical landslide dams with complete data were chosen for the stability assessment of landslide dams, as listed in Table 4.…”

Section: The Indexmentioning

confidence: 99%

A statistical risk assessment model of hazard chain induced by landslide and its application to the Baige landslide

Yang

Xiao

et al. 2022

Preprint

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Landslides are usually caused by geological processes such as rainstorms or earthquakes and may have a massive impact on human production and life. The hazard chain of landslide-river blockage-outburst flood is the most common hazard chain caused by landslides. A database based on existing landslide cases was established in this paper to investigate the assessment formulas of river blocking risk, dam stability, and peak flood discharge after the dam break. A risk assessment model of the landslide-river blocking-breaching hazard chain was established with the vulnerability downstream. The case of the Baige landslide verifies the applicability of the model. This model can be used in a landslide-prone area to predict whether it will form a relatively massive river blockage after the landslide occurs, whether the landslide dam formed by river blockage will breach in a short time, as well as the impact of the outburst flood on the downstream area.

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“…Huang et al (2020), Wubalem (2021), Huangfu et al (2021), and Soma et al (2019) used semisupervised multiple-layer perceptron, information value, a multiple logistic regression algorithm, frequency ratio (FR), and logistic regression (LR) models to produce LSM. Among these different evaluation methods, the most common and reliable one is logistic regression (Ayalew and Yamagishi, 2005;Kalantar et al, 2018;Shan et al, 2020). Some scholars have discovered that the longitude of LRM can be improved by changing the parameters or models that optimize LRM.…”

Section: Introductionmentioning

confidence: 99%

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Sun

Wen

et al. 2021

Front. Earth Sci.

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This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, was selected as the study area since it experienced 1,522 landslides from 2001 to 2016. Second, 22 factors were selected as the initial conditioning factors, and a geospatial database was established with a grid of 30 m precision. Factor detection of the geographic detector and the stepwise regression method included in logistic regression were used to screen out the dominant factors from the database. Then, based on the sample dataset with a 1:10 ratio of landslides and nonlandslides, 10-fold cross validation was used to select the optimized sample to train the logistic regression model of landslide susceptibility in the study area. Finally, the accuracy and efficiency of the two models before and after screening out the dominant factors were evaluated and compared. The results showed that the total accuracy of the two models was both more than 0.9, and the area under the curve value of the receiver operating characteristic curve was more than 0.8, indicating that the models before and after screening factor both had high reliability and good prediction ability. Besides, the screened factors had an active leading role in the geospatial distribution of the historical landslide, indicating that the screened dominant factors have individual rationality. Improving the geospatial agreement between landslide susceptibility and actual landslide-prone by the screening of dominant factors and the optimization of the training samples, a simple, efficient, and reliable logistic-regression–based landslide susceptibility model can be constructed.

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Rapid prediction of landslide dam stability using the logistic regression method

Cited by 31 publications

References 42 publications

Review on risk assessments of dammed lakes

Review on risk assessments of dammed lakes

A statistical risk assessment model of hazard chain induced by landslide and its application to the Baige landslide

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

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