Multimodal method for landslide risk analysis

Pollock, William; Grant, Alex; Wartman, Joseph; Abou-Jaoude, Grace

doi:10.1016/j.mex.2019.04.012

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…At least two forms of movement (falling, toppling, sliding, spreading, and flowing) occur in a complex landslide. Multiple landslides are characterized by the emergence of the same type of movement multiple times 114 .…”

Section: Methodsmentioning

confidence: 99%

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Kainthura

Sharma

2022

Sci Rep

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Natural disasters always have a damaging effect on our way of life. Landslides cause serious damage to both human and natural resources around the world. In this paper, the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared. In this approach, the Rough Set theory coupled with five different models namely Bayesian Network (HBNRS), Backpropagation Neural Network (HBPNNRS), Bagging (HBRS), XGBoost (HXGBRS), and Random Forest (HRFRS) were taken into account. The database for the models development was prepared using fifteen conditioning factors that had 373 landslide and 181 non-landslide locations that were then randomly divided into training and testing locations with a ratio of 75%:25%. The appropriateness and predictability of these conditioning factors were assessed using the multi-collinearity test and the least absolute shrinkage and selection operator approach. The accuracy, sensitivity, specificity, precision, and F-Measures, and the area under the curve (AUC)-receiver operating characteristics curve, were used to evaluate and compare the performance of the individual and hybrid created models. The findings indicate that the constructed hybrid model HXGBRS (AUC = 0.937, Precision = 0.946, F1-score = 0.926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS). Importantly, when the fusion is performed with the rough set method, the prediction capability of each model is improved. Simultaneously, the HXGBRS model proposed shows superior stability and can effectively avoid overfitting. After the core modules were developed, the user-friendly platform was designed as an integrated GIS environment using dynamic maps for effective landslide prediction in large prone areas. Users can predict the probability of landslide occurrence for selected region by changing the values of a conditioning factors. The created approach could be beneficial for predicting the impact of landslides on slopes and tracking landslides along national routes.

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Section: Methodsmentioning

confidence: 99%

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Kainthura

Sharma

2022

Sci Rep

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“…Previous research on the evaluation of landslides, has mainly been conducted on three aspects: landslide sensitivity 9 – 12 , landslide risk 13 – 15 and landslide susceptibility 16 . Among then, landslide susceptibility can be defined as the spatial probability of landslide occurrence based on a set of geological and environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR

Fan

Liu

Luo

et al. 2023

Sci Rep

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Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas.

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“…Recently, Grant et al (2016) and Kaafarani et al (2019) assessed the rugged terrain under earthquake and rainfall events at a macro level (regional scale) using a multimodal approach, where slopes were analyzed based on their geology and degree of inclination. Pollock et al (2019), then, evaluated the risks associated with the predicted landslide hazards. This regional-scale work was performed in a GIS framework, based on the geologic map of Lebanon, a 15-m digital elevation map (DEM), earthquake peak ground acceleration (PGA) maps and rainfall data.…”

Section: List Of Figuresmentioning

confidence: 99%

Landslide Hazard and Risk Level Assessment of Quarried Slopes in Lebanon using Drone Imagery. (c2020)

Kaafarani¹

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Human activities are continuously altering the geometry and steepness of natural slopes. When left exposed and unprotected, these slopes become susceptible to slide due to natural triggering factors like earthquakes and rainfall. Quarrying activities, in particular, contribute largely to slope failures worldwide, especially when unorganized and chaotic. In Lebanon, quarries are scattered randomly across the country and lack proper urban planning and management. Regional scale maps have been recently generated in a Geographic Information System (GIS) platform to identify hazard and risk areas for co-seismic and rainfall induced landslides; however, their applicability to altered “quarried” slopes has not been tested. In this research, we present a thorough methodology to assess, at a site-specific level, the hazard and risk levels of quarried slopes under three conditions: dry condition, heavy rainfall condition, and seismic condition. The aim is to ascertain the degree of accuracy of the regional scale maps in predicting landslides in quarried areas. A jointed limestone quarry in Bafliye, South of Lebanon, was mapped with a DJI Phantom 4 V2.0 drone, recreated as a 3D scene, assessed kinematically, and then analyzed using limit equilibrium and numerical modelling techniques. At failure, the maximum runout distance and the corresponding angle of reach were determined. The studied slope showed signs of failure under seismic events having a 10% chance of exceedance in 50 years, and under rainfall events having a 10-year return period with a relatively small runout distance of 7.76 m, yielding a low risk failure. The output of safety factors matched between the site specific and the regional scale analyses, while the failing mass volume and the corresponding risk levels did not. This indicated that the wedge failure analysis used in the regional scale analysis is adequate in predicting only hazard levels at quarried sites.

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Multimodal method for landslide risk analysis

Abstract: Graphical abstract

Cited by 14 publications

References 44 publications

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR

Landslide Hazard and Risk Level Assessment of Quarried Slopes in Lebanon using Drone Imagery. (c2020)

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