Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Li, Langping; Lan, Hengxing

doi:10.3390/ijerph17218055

Cited by 15 publications

(8 citation statements)

References 82 publications

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“…point 1a) ensures that the simulations will include all of the potential sources that could develop into rockfalls and whose runout could concern the railway network (this work does not attempt to account for any anthropogenic mass displacements in the vicinities of the railway track). We favor SU as suitable mapping units for the description of landslide phenomena (Alvioli et al, 2016;Camilo et al, 2017;Schlögel et al, 2018;Bornaetxea et al, 2018;Tanyaş et al, 2019a,b;Jacobs et al, 2020;Amato et al, 2020;Chen et al, 2020;Li and Lan, 2020). More specifically, we adopted SUs instead of a geometric buffer around the track, because rockfall trajectories initiated in a given SU will be bounded within the SU, with reasonable confidence.…”

Section: Identification Of Rockfall Source Areasmentioning

confidence: 99%

Rockfall susceptibility and network-ranked susceptibility along the Italian railway

Alvioli,

Santangelo,

Fiorucci

et al. 2021

Preprint

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Rockfalls pose a substantial threat to ground transportation, due to their rapidity, destructive potential and high probability of occurrence on steep topographies, often found along roads and railway routes. Various factors can trigger rockfalls, including intense rainfall and seismic activity, and diverse phenomena affect their probability of occurrence. Approaches for the assessment of rockfall susceptibility range from purely phenomenological methods and statistical methods, suitable for modeling large areas, to purely deterministic ones, usually easier to use in local analyses. A common requirement is the need to locate potential detachment points, often found uphill on cliffs, and the subsequent assessment of the runout areas of rockfalls stemming from such points.Here, we apply a physically based model for the calculation of rockfall trajectories along the whole Italian railway network, within a corridor of total length of about 17,000 km and varying width. We propose a data-driven method for the location of rockfall source points based on expert mapping of potential source areas on sample representative locations. Using empirical distributions of gridded slope values in source areas mapped by experts, we derived probabilistic maps of rockfall sources in the proximity of the railway network, regardless of a particular trigger.Source areas act as starting points of simulated trajectories, within the three-dimensional model STONE. The program provides a pixel-by-pixel trajectory count, which we classify into a susceptibility map. We obtained the map analyzing the railway track as a collection of segments, for which we provide segment-wise rockfall susceptibility.Eventually, we considered an equivalent graph representation of the network, which helps classifying the segments both on the basis of rockfall susceptibility and the role of each segment in the network, resulting in a network-ranked susceptibility.

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Section: Identification Of Rockfall Source Areasmentioning

confidence: 99%

Rockfall susceptibility and network-ranked susceptibility along the Italian railway

Alvioli,

Santangelo,

Fiorucci

et al. 2021

Preprint

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“…Concurrently, Li et al significantly increased the value of the Receiver Operating Characteristic (ROC) of the LSM. The slope unit's landslide susceptibility value is determined by combining the estimated likelihood of a landslide occurring (spatial probability) with the anticipated area of the slope units where a landslide may occur (Li and Lan, 2020). The structure of the convolutional neural network (CNN) is inspired by the perception of spatial features in the biological visual system.…”

Section: Introductionmentioning

confidence: 99%

3DCNN landslide susceptibility considering spatial-factor features

Liu

et al. 2023

Front. Environ. Sci.

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Effective landslide disaster risk management contributes to sustainable development. A useful method for emergency management and landslide avoidance is Landslide Susceptibility Mapping (LSM). The statistical landslide susceptibility prediction model based on slope unit ignores the re-lationship between landslide triggering factors and spatial characteristics. It disregards the influence of adjacent image elements around the slope-unit element. Therefore, this paper proposes a hardwired kernels-3DCNN approach to LSMs considering spatial-factor features. This method effectively solved the problem of low dimensionality of 3D convolution in the hazard factor layer by combining Prewitt operators to enhance the generation of multi-level 3D cube input data sets. The susceptibility value of the target area was then calculated using a 3D convolution to extract spatial and multi-factor features between them. A geospatial dataset of 402 landslides in Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province, China, was created for this study. Nine landslide trigger factors, including topography and geomorphology, stratigraphic lithology, rainfall, and human influences, were employed in the LSM. The research area’s pixel points’ landslide probabilities were then estimated by the training model, yielding the sensitivity maps. According to the results of this study, the 3DCNN model performs better when spatial information are included and trigger variables are taken into account, as shown by the high values of the area under the receiver operating characteristic curve (AUC) and other quantitative metrics. The proposed model outperforms CNN and SVM in AUC by 4.3% and 5.9%, respectively. Thus, the 3DCNN model, with the addition of spatial attributes, effectively improves the prediction accuracy of LSM. At the same time, this paper found that the model performance of the proposed method is related to the actual space size of the landslide body by comparing the impact of input data of different scales on the proposed method.

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“…With the continuous development of mathematical models and computer technology, the research methods of regional LSA are still being innovated. The majority of conventional studies are mathematically and statistically based methods [18][19][20][21]. Some researchers used mathematical statistical models such as hierarchical analysis, interval rough number-hierarchical analysis, entropy power method-hierarchical analysis to evaluate and analyze the distribution and development characteristics of landslides [22], others used information value, and weight of evidence methods to determine the landslide susceptibility, and used validation methods such as applying a receiver operating characteristic curve (ROC), proportional correct classification, and seed cell area index (SCAI) for evaluation [23,24].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

Rong

et al. 2021

Remote Sensing

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Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.

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Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Cited by 15 publications

References 82 publications

Rockfall susceptibility and network-ranked susceptibility along the Italian railway

Rockfall susceptibility and network-ranked susceptibility along the Italian railway

3DCNN landslide susceptibility considering spatial-factor features

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

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