Prediction of shallow landslide occurrence: Validation of a physically-based approach through a real case study

Schilirò, Luca; Montrasio, Lorella; Mugnozza, Gabriele Scarascia

doi:10.1016/j.scitotenv.2016.06.124

Cited by 72 publications

(26 citation statements)

References 44 publications

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“…As stated previously by Schilirò et al. (), rainfall‐induced shallow landslides typically occur because of small failures of natural slopes that collapse devastatingly without warning. Although they generally involve small volume of soils (generally less than 2.0 m), they pose a grave threat and risk to life, properties, environment, and infrastructure.…”

Section: Introductionmentioning

confidence: 77%

“…These types include deep-seated, falls, topples, rotational, flows, lateral spreads, complex, and shallow and translational landslides (Pradhan, 2011). Therefore, shallow landslides are categorized as a type of landslide in which the sliding surface is located within the soil mantle or weathered bedrock, typically to a depth from a few decimeters to several meters (Schilirò, Montrasio, & Mugnozza, 2016;Trigila, Iadanza, Esposito, & Scarascia-Mugnozza, 2015). Moreover, the surface of the slope in steep and hilly mountainous areas is often underlain by a plane of weakness lying parallel to it and shallow landslides become predominant.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

et al. 2019

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The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators—accuracy, precision, and the root mean square error (RMSE)—were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central‐east Africa.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

et al. 2019

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“…1). This area has been affected several 5 times by catastrophic shallow landslide phenomena in the past; including the 1 October 2009 disaster, which has been analysed and described in several studies (Cama et al, 2017;Schilirò et al, 2015Schilirò et al, , 2016Schilirò et al, 2015;Stancanelli et al, 2017).…”

Section: Dataset: Generation Of Synthetic Rainfall and Landslide Datamentioning

confidence: 99%

Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

Peres¹,

Cancelliere²,

Greco³

et al. 2017

Preprint

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Uncertainty in rainfall datasets and landslide inventories is known to have negative impacts on the assessment of landslidetriggering thresholds. In this paper, we perform a quantitative analysis of the impacts that the uncertain knowledge of landslide initiation instants have on the assessment of landslide intensity-duration early warning thresholds. The analysis is based on an ideal synthetic database of rainfall and landslide data, generated by coupling a stochastic rainfall generator and a physically 15 based hydrological and slope stability model. This dataset is then perturbed according to hypothetical "reporting scenarios", that allow to simulate possible errors in landslide triggering instants, as derived from historical archives. The impact of these errors is analysed by combining different criteria to single-out rainfall events from a continuous series and different temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant. Errors influence thresholds in a way that they are generally underestimated. Potentially, the amount of the 20 underestimation can be enough to induce an excessive number of false positives, hence limiting possible landslide mitigation benefits. Moreover, the uncertain knowledge of triggering rainfall, limits the possibility to set up links between thresholds and physio-geographical factors.

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“…These methods can be generally categorized into two groups. The first is a deterministic or engineering approach based on mathematical models of the physical mechanisms that control slope failure, e.g., TRIGRS [27,28]. The significant limitation of this kind method is the requirement for material data (mechanical properties, water saturation, etc.)…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Wang

Bai

et al. 2018

Water

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Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.

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Prediction of shallow landslide occurrence: Validation of a physically-based approach through a real case study

Cited by 72 publications

References 44 publications

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

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