Common Patterns Among Different Landslide Susceptibility Models of the Same Region

Lee, Chyi-Tyi; Chung, Chih‐Chung

doi:10.1007/978-3-319-53498-5_106

Cited by 5 publications

(3 citation statements)

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“…The findings of the current study that the relatively excellent predictive performance and low uncertainty of the landslide susceptibility models established using multi-year landslide inventories verifies the advantage of using a combination of event-based inventories and confirms the previous study results. The relatively high predictive abilities of the landslide susceptibility models, built by the combination of different event-based landslide inventories, have been thought to be related to their bigger landslide sample size and the wider numerical range of rainfall parameters in the training sample [23,54,55], or to their lower concentration of landslides in areas with the same lithology and a lower collinearity between rainfall parameters and lithology [56,57].…”

Section: Comparison Of the Performance Of Event-based And Multi-year ...mentioning

confidence: 99%

“…We also compared these landslide susceptibility maps, in order to analyze the correlations in the spatial distribution of susceptibility index between the multi-year PM ensemble model and other models. Rather than performing the mutual subtraction algorithm [6,[58][59][60] or the histogram matching method [55,56], we calculated the Spearman's rank correlation coefficient to assess the degree of difference between the susceptibility maps of the optimal model and other models. As shown in Table 13, the correlation coefficient of the single models ranged from 0.811 to 0.946, with an average of 0.912, which was lower than the 0.940-1.00 correlation coefficient range of the ensemble models.…”

Section: Correlations Between the Susceptibility Maps Of The Optimal ...mentioning

confidence: 99%

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Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

Lin

2022

Water

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While multi-year and event-based landslide inventories are both commonly used in landslide susceptibility analysis, most areas lack multi-year landslide inventories, and the analysis results obtained from the use of event-based landslide inventories are very sensitive to the choice of event. Based on 24 event-based landslide inventories for the Shihmen watershed from 1996 to 2015, this study established five event-based single landslide susceptibility models employing logistic regression, random forest, support vector machine, kernel logistic regression, and gradient boosting decision tree methods. The ensemble methods, involving calculating the mean of the susceptibility indexes (PM), median of the susceptibility indexes (PME), weighted mean of the susceptibility indexes (PMW), and committee average of binary susceptibility values (CA) of the five single models were then used to establish four event-based ensemble landslide susceptibility models. After establishing nine landslide susceptibility models, using each inventory from the 24 event-based landslide inventories or a multi-year landslide inventory, we identified the differences in landslide susceptibility maps attributable to the different landslide inventories and modeling methods, and used the area under the receiver operating characteristic curve to assess the accuracy of the models. The results indicated that an ensemble model based on a multi-year inventory can obtain excellent predictive accuracy. The predictive accuracy of multi-year landslide susceptibility models is found to be superior to that of event-based models. In addition, the higher predictive accuracy of ensemble landslide susceptibility models than that of single models implied that these ensemble methods were robust for enhancing the model’s predictive performance in the study area. When employing event-based landslide inventories in modeling, PM ensemble models offer the best predictive ability, according to the Kruskal–Wallis test results. Areas with a high mean susceptibility index and low standard deviation, identified using the 24 PM ensemble models based on different event-based landslide inventories, constitute places where landslide mitigation measures should be prioritized.

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Section: Comparison Of the Performance Of Event-based And Multi-year ...mentioning

confidence: 99%

Section: Correlations Between the Susceptibility Maps Of The Optimal ...mentioning

confidence: 99%

Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

Lin

2022

Water

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“…The hydrological models employ infiltration models to determine the critical rainfall triggering landslides, which requires the estimation and validation of soil parameters over large areas, and therefore makes these models impractical for regional-scale applications. The approaches based on exceedance probability can be further subdivided into two types, where the first type employs a landslide inventory induced by a single rainfall event and rainfall data for that event to analyze the return period of the landslide event [8,23,24], and the second type employs a long-term landslide inventory to calculate the exceedance probability for the occurrence of landslides. Concerning the latter type, the Poisson probability model [17,[25][26][27][28][29], binomial probability model, and empirical model [20] are commonly used to analyze the recurrence probability of landslide events.…”

Section: Introductionmentioning

confidence: 99%

A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors

Yeh

2020

Water

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The prediction and advanced warning of landslide hazards in large-scale areas must deal with a large amount of uncertainty, therefore a growing number of studies are using stochastic models to analyze the probability of landslide occurrences. In this study, we used a modified Thiessen’s polygon method to divide the research area into several rain gauge control areas, and divided the control areas into slope units reflecting the topographic characteristics to enhance the spatial resolution of a landslide probability model. We used a 2000–2015 long-term landslide inventory, daily rainfall, and effective accumulated rainfall to estimate the rainfall threshold that can trigger landslides. We then employed a Poisson probability model and historical rainfall data from 1987 to 2016 to calculate the exceedance probability that rainfall events will exceed the threshold value. We calculated the number of landslides occurring from the events when rainfall exceeds the threshold value in the slope units to estimate the probability that a landslide will occur in this situation. Lastly, we employed the concept of conditional probability by multiplying this probability with the exceedance probability of rainfall events exceeding the threshold value, which yielded the probability that a landslide will occur in each slope unit for one year. The results indicated the slope units with high probability that at least one rainfall event will exceed the threshold value at the same time that one landslide will occur within any one year are largely located in the southwestern part of the Taipei Water Source Domain, and the highest probability is 0.26. These slope units are located in parts of the study area with relatively weak lithology, high elevations, and steep slopes. Compared with probability models based solely on landslide inventories, our proposed landslide probability model, combined with a long-term landslide inventory and rainfall factors, can avoid problems resulting from an incomplete landslide inventory, and can also be used to estimate landslide occurrence probability based on future potential changes in rainfall.

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Landslide Hazard, Risk Assessment and Prediction: Landslide Inventories and Susceptibility, Hazard Mapping Methods, Damage Potential—Part 2

Arbanas

2017

Advancing Culture of Living With Landslides

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Common Patterns Among Different Landslide Susceptibility Models of the Same Region

Cited by 5 publications

References 10 publications

Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors

Landslide Hazard, Risk Assessment and Prediction: Landslide Inventories and Susceptibility, Hazard Mapping Methods, Damage Potential—Part 2

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