Schützt der Wald vor Rutschungen? Hinweise aus der WSL-Rutschungsdatenbank

Rickli, Christian; Graf, Frank; Bebi, Peter; Bast, Alexander; Loup, Bernard; McArdell, Brian W.

doi:10.3188/szf.2019.0310

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…The first dataset (hereafter "HMDB") is based on a comprehensive database of shallow landslides and hillslope debris flows created by Rickli et al (2016Rickli et al ( , 2019 such as the dimensions of the landslides, site characteristics, and runout characteristics (Rickli and Graf, 2009;WSL and BAFU, 2018). If values could not be measured in the field (e.g., because of terrain changes since the event), the database may contain estimated values that are marked accordingly (Rickli et al, 2016).…”

Section: Landslide Inventoriesmentioning

confidence: 99%

Predicting the thickness of shallow landslides in Switzerland using machine learning

Schaller,

Dorren,

Schwarz

et al. 2024

Preprint

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Abstract. Landslide thickness is a key parameter in various types of models used to simulate landslide susceptibility. In this study, we developed a model providing improved predictions of potential shallow landslide thickness in Switzerland. We tested three machine learning models based on random forests, generalized additive model, and linear regression and compared the results to three existing models that link soil thickness to slope and elevation. The models were calibrated using data from two field inventories in Switzerland ("HMDB" with 709 records and "KtBE" with 517 records). We explored 37 different covariates including metrics on terrain, geomorphology, vegetation height, and lithology at three different cell sizes. To train the machine learning models, 21 variables were chosen based on the variable importance derived from random forest models and expert judgement. Our results show that the machine learning models consistently outperformed the existing models by reducing the mean absolute error by at least 17 %. The random forests models produced a mean absolute error of 0.25 m for the HMDB and 0.19 m for the KtBE data. Models based on machine learning substantially improve the prediction of landslide thickness, offering refined input for enhancing the performance of slope stability simulations.

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Section: Landslide Inventoriesmentioning

confidence: 99%

Predicting the thickness of shallow landslides in Switzerland using machine learning

Schaller,

Dorren,

Schwarz

et al. 2024

Preprint

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“…However, it should be representative of the conditions in the area of interest as much as possible. A dataset of 668 shallow landslides that occurred between 1997 and 2012 in Switzerland has been created by the Swiss Federal Office for the Environment (BAFU; Rickli et al, 2019). Statistical information on the landslides can be seen in Fig.…”

Section: Landslide Inventorymentioning

confidence: 99%

Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations

Zadelhoff

Albaba

Cohen³

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Shallow landslides pose a risk to infrastructure and residential areas. Therefore, we developed SlideforMAP, a probabilistic model that allows for a regional assessment of shallow-landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SlideforMAP uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated on observed events. For each generated landslide, SlideforMAP calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters are assigned to the generated landslides from log-normal distributions based on mean and standard deviation values representative of the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed by root proximity and root strength derived from single-tree-detection data. The ratio of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. We performed a calibration of SlideforMAP for three test areas in Switzerland with a reliable landslide inventory by randomly generating 1000 combinations of model parameters and then maximizing the area under the curve (AUC) of the receiver operation curve. The test areas are located in mountainous areas ranging from 0.5–7.5 km2 with mean slope gradients from 18–28∘. The density of inventoried historical landslides varies from 5–59 slides km−2. AUC values between 0.64 and 0.93 with the implementation of single-tree detection indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modelling of shallow-landslide probability are the soil thickness, soil cohesion and the precipitation intensity / transmissivity ratio. Furthermore, we show that the inclusion of single-tree detection improves overall model performance compared to assumptions of uniform vegetation. In conclusion, our study shows that the approach used in SlideforMAP can reproduce observed shallow-landslide occurrence at a catchment scale.

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“…in Switzerland has been created by the Swiss Federal Office for the Environment (Rickli et al, 2019). Statistical information after heavy rainfall in August.…”

Section: Landslide Inventorymentioning

confidence: 99%

Introducing SlideforMap; a probabilistic finite slope approach for modelling shallow landslide probability in forested situations

Zadelhoff

Albaba

Cohen³

et al. 2021

Preprint

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Abstract. Worldwide, shallow landslides repeatedly pose a risk to infrastructure and residential areas. To analyse and predict the risk posed by shallow landslides, a wide range of scientific methods and tools to model shallow landslide probability exist for both local and regional scale However, most of these tools do not take the protective effect of vegetation into account. Therefore, we developed SlideforMap (SfM), which is a probabilistic model that allows for a regional assessment of shallow landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SfM uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated from observed events. For each randomly generated landslide, SfM calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are assigned to the generated landslides from normal distributions based on mean and standard deviation values representative for the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed based on root proximity and root strength derived from single tree detection data. Ultimately, the fraction of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. Inputs for the model are a digital elevation model, a topographic wetness index and a file containing positions and dimensions of trees. We performed a calibration of SfM for three test areas in Switzerland with a reliable landslide inventory, by randomly generating 1000 combinations of model parameters and then maximising the Area Under the Curve (AUC) of the receiver operation curve (ROC). These test areas are located in mountainous areas ranging from 0.5–7.5 km2, with varying mean slope gradients (18–28°). The density of inventoried historical landslides varied from 5–59 slides/km2. AUC values between 0.67 and 0.92 indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modeling of shallow landslide probability are the soil depth, soil cohesion and the root reinforcement. Further, the use of single tree detection in the computation of root reinforcement significantly improved model accuracy compared to the assumption of a single constant value of root reinforcement within a forest stand. In conclusion, our study showed that the approach used in SfM can reproduce observed shallow landslide occurrence at a catchment scale.

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Schützt der Wald vor Rutschungen? Hinweise aus der WSL-Rutschungsdatenbank

Cited by 5 publications

References 4 publications

Predicting the thickness of shallow landslides in Switzerland using machine learning

Predicting the thickness of shallow landslides in Switzerland using machine learning

Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations

Introducing SlideforMap; a probabilistic finite slope approach for modelling shallow landslide probability in forested situations

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