Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution

Lee, Jung Hyun; Kim, Hanbeen; Park, Hyuck‐Jin; Heo, Jun Haeng

doi:10.1007/s10346-020-01502-7

Cited by 36 publications

(13 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An indirect approach based on the frequency of landslide-triggering events (i.e., earthquake or rainfall events) is, therefore, proposed. In this approach, recognizing that landslides are mainly caused by rainfall events, the temporal probability of such rainfall events is adopted as the temporal probability of landslide occurrence [42][43][44][45][46][47][48][49]. A rainfall threshold for landslide occurrence is determined and then historical rainfall data are analyzed to derive the probability that the rainfall threshold will be exceeded by a certain rainfall event (the exceedance probability).…”

Section: Introductionmentioning

confidence: 99%

“…A rainfall threshold for landslide occurrence is determined and then historical rainfall data are analyzed to derive the probability that the rainfall threshold will be exceeded by a certain rainfall event (the exceedance probability). The rainfall exceedance probability is observed as an effective surrogate for temporal landslide probability [37,42,43,45,46,48]. The advantages of this approach are that a complete multitemporal inventory is not required and that temporal probability can be estimated wherever historical rainfall records, which can be easily obtained from rainfall gauges, are available.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach

et al. 2022

Self Cite

View full text Add to dashboard Cite

The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The system uses daily rainfall data, rainfall threshold methods and regional slope stability model based on instantaneous rainfall infiltration and grids to predict the occurrence of landslides. Lee Jung Hyun [8] used the Gumbel model to estimate the probability in exceeding the rainfall threshold, and combined the time probability of landslide occurrence with landslide sensitivity result of multi-layer perceptron model to estimate the risk of landslides at different time periods in the future.…”

Section: Introductionmentioning

confidence: 99%

Risk Identification of Heavy Rain-induced Muck soil Landslide

Feng¹

2021

converter

View full text Add to dashboard Cite

Based on Monte Carlo method, this paper calculates landslide probability of muck soil slopes under different rainfall time, rainfall intensity, soil permeability coefficient and slope angle, thus obtaining the probability samples of muck soil landslides. On this basis, logistic regression method of nonlinear classification is used for data fitting and analysis, thus establishing nonlinear function . Function expression is derived by data fitting, and a landslide probability evaluation model is constructed. Based on analysis of engineering examples, the error between this method and the numerical calculation results is within 10%, and the evaluation results are reasonable. It provides theoretical support for rapid identification of muck soil landslide risk under heavy rain conditions.

show abstract

“…Few attempts have been performed to exploit the possibility of using data-driven techniques which integrate static predisposing and dynamic triggering factors (Wang and Sassa 2006;Lee et al 2020;Lombardo et al 2014Lombardo et al , 2020. These approaches aimed to fill the intrinsic gaps of rainfall thresholds and physically based models, especially to: (i) take into account both predisposing geomorphological, geological, and hydrological features and rainfall triggering factors; (ii) be effective at local and regional scales; and (iii) represent the variation of unstable areas in time during a particular rainfall event.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these approaches insert parameters of a rainfall event, collected through rain gauges or radar instruments, within the set of predictors of a data-driven algorithm to model the probability of occurrence of the triggered slope instabilities (Dai and Lee 2003;Ayalew and Yamagishi 2005;Wang and Sassa 2006;Chang et al 2008;Chang and Chiang 2009;Capecchi et al 2015;Lee et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale

et al. 2020

View full text Add to dashboard Cite

A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided.

show abstract

Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution

Cited by 36 publications

References 93 publications

Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach

Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach

Risk Identification of Heavy Rain-induced Muck soil Landslide

Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale

Contact Info

Product

Resources

About