Guidance Index for Shallow Landslide Hazard Analysis

Cullen, Cheila; Alsuhili, Rafea; Khanbilvardi, R.

doi:10.3390/rs8100866

Cited by 27 publications

(12 citation statements)

References 29 publications

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“…Vertical and horizontal ravines were also seen in this section, with a relative height difference greater than 100 m and slopes between 15 to 35 • . Slope degrees with obvious changes had a great influence on slope stability (Chang and Kim, 2004;Hu et al, 2015). The exposed rocks in this area were mainly shale and belonged to the sliding-prone rock group.…”

Section: Analysis Of Risk Assessment Resultsmentioning

confidence: 91%

“…Based on selection principles of the indicator system and the formation mechanism of landslide geological hazards, as few indicators as possible were selected to reflect the degree of danger posed by the landslide as accurately as possible (Avalon Cullen et al, 2016;Jaiswal et al, 2010;Ray et al, 2007). The internal factors in these indicators included topography, geological structure, stratigraphic lithology and surface coverage.…”

Section: Assessment Indicatorsmentioning

confidence: 99%

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Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Xiong

Sun

Zhang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a susceptibility assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg–Marquardt back propagation (LM-BP) neural network model was used to conduct the susceptibility assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide susceptibility risk zoning map was obtained based on susceptibility and vulnerability assessment. The results show that about 70 % of the slopes were in high-susceptibility areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines, and safely operating pipelines in the Guangyuan area and other segments of the LCC oil pipeline.

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Section: Analysis Of Risk Assessment Resultsmentioning

confidence: 91%

Section: Assessment Indicatorsmentioning

confidence: 99%

Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Xiong

Sun

Zhang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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show abstract

“…1). High seismicity (Delvaux et al, 2017), intense rainfall (Monsieurs et al, 2018b), deeply weathered substrates Broeckx et al (2018), and distribution of landslides in the western branch of the East African Rift, comprising 29 landslides in mining areas (triangles) and 145 landslides outside mining areas (dots) of which the red dots are landslides associated with antecedent rainfall less than 5 mm. Only the black dots (143 landslides) are used for calibrating the rainfall thresholds.…”

Section: Landslides In the Wearmentioning

confidence: 99%

Supplementary material to "A susceptibility-based rainfall threshold approach for landslide occurrence"

Monsieurs¹,

Dewitte²,

Demoulin³

2018

Preprint

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Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or eventduration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α ± α) • S (β± β) , where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and α and β are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

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“…Landslides are one of the most common geological disasters worldwide and cause considerable damage to public infrastructure and human life every year [1,2]. The predictive modeling of landslide occurrence is one of the main challenges in geological hazard research.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Zhu

Huang

Fan

et al. 2020

Sensors

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Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

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Guidance Index for Shallow Landslide Hazard Analysis

Cited by 27 publications

References 29 publications

Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Supplementary material to "A susceptibility-based rainfall threshold approach for landslide occurrence"

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

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Guidance Index for Shallow Landslide Hazard Analysis

Cited by 27 publications

References 29 publications

Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Supplementary material to &quot;A susceptibility-based rainfall threshold approach for landslide occurrence&quot;

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

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Supplementary material to "A susceptibility-based rainfall threshold approach for landslide occurrence"