Slope Stability Assessment Using Trigger Parameters and SINMAP Methods on Tamblingan-Buyan Ancient Mountain Area in Buleleng Regency, Bali

Sinarta, I Nengah; Rifa’i, Ahmad; Fathani, Teuku Faisal; Wilopo, Wahyu

doi:10.3390/geosciences7040110

Cited by 16 publications

(18 citation statements)

References 11 publications

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“…By applying the SINMAP method, the level of susceptibility is detected based on the FS, which categorizes the stability into different classes. As confirmed previously (Nery & Vieira, ; Pack et al., ; Sinarta et al., ), the classes show the conditions of the study area based on the SI value. Conversely, the SMCE method uses the overlay techniques of the weighted and standardized landslide causal factors in the form of indicator maps.…”

Section: Methodssupporting

confidence: 85%

“…In addition, this process‐driven model plays a paramount role in determining the FS for the disparity of the force of collapsing and the triggering causes (Dou et al., ; Van Den Eeckhaut et al., ). The deterministic modeling approach is illustrated by the following equation (Nery & Vieira, ; Sinarta et al., ; Vieira, Fernandes, Augusto Filho, Martins, & Montgomery, ):

\begin{matrix} true \\ right FS = \frac{C_{r} + C_{s} + \cos^{2} θ ([], ρ_{s} g (D - D_{w}) + (ρ_{s} g - ρ_{w} g) D_{w}) \tan ϕ}{D ρ_{s} g \sin θ \cos θ}, \end{matrix}

where C r is the soil root cohesion (N/m 2 ), C s is the soil cohesion (N/m 2 ), θ is the slope angle or slope inclination, ρ s is the wet soil density (kg/m 3 ), ρ w is the water density (kg/m 3 ), g is gravitational acceleration or gravity force (9.81 m/s 2 ), D is the vertical soil depth (m), D w is the vertical height of the water table in the soil layer (m), and φ is the soil internal friction angle. Therefore, as discussed by previous authors (Batjes, ; Yalcin, ; Zizioli et al., ), the value of the slope inclination ( θ ) can be computed by applying the arc tangent slope formula S .…”

Section: Methodsmentioning

confidence: 99%

“…It is a raster-based predictive method within which coupled hydrological infinity of the slope is considered in order to detect landslide susceptible zones (Montgomery & Dietrich, 1994;Pack, Tarboton, & Goodwin, 2001;Sinarta, Rifa'i, Faisal Fathani, & Wilopo, 2017). Thus, SINMAP is implemented in ArcGIS-ArcMap and is applicable to shallow landslides (Pack et al, 2001;Sinarta et al, 2017). Generally, the data set required to implement the SINMAP model consists of the grid-digital elevation model (DEM).…”

Section: Data Sets For the Sinmap Modelmentioning

confidence: 99%

“…Generally, the data set required to implement the SINMAP model consists of the grid-digital elevation model (DEM). It is therefore believed that the spatial accuracy and precision of susceptibility maps is strongly dependent on DEM resolution (Sinarta et al, 2017). However, the available DEM for Rwanda (30 m resolution) was in tagged image file format (TIFF) images format, which is not compatible with the SINMAP method.…”

Section: Data Sets For the Sinmap Modelmentioning

confidence: 99%

“…In addition, the implementation of the SINMAP model requires physical soil properties (Table I). The SIN-MAP method uses soil thickness, strength properties (from geotechnical data), and hydraulic conductivity (Pack et al, 2001;Sinarta et al, 2017). Thus, soil data properties used in this study were derived from previous studies (Table I).…”

Section: Data Sets For the Sinmap Modelmentioning

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: Methodssupporting

confidence: 85%