A PFE/IE – SPH joint approach to model landslides from initiation to propagation

Lin, Chih-Hong; Pastor, Manuel; Liu, Xiaoqing; Lin, Chaoning; Qi, Huijun; Sheng, Taozhen

doi:10.1016/j.compgeo.2019.103153

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…Various mass movement modeling techniques have been developed such as Machine Learning Model (Bui et al, 2020), Deep Learning Neural Network ( et al, 2020, Cellular Automata ( Bała, 2010), Statistical approaches ( Physical Simulation (Wibowo, 2011;Wibowo, 2016Ran et al, 2018Weidner et al, 2019;Velásquez, 2020) and Vibration Simulation ( Aharonov, 2006;Chen et al 2020 Numerical model (raster) based on physical characteristics of materials is being such as DAN and DAN 3D based on the application of quantum mechanics to estimate the average depth of material of mass movements ( 2009). Another numerical model is uses raster data as a basis for calculating mass conservation and mometum of event (Beguería et al, 2009 Li et al, 2020;Shen et al, 2020), Smoothed particle hydrodynamics (Lin et al, 2019), Unsupervised factor optimization (Sameen et al, 2020), DEM optimization (Ville et al, 2015;Sarma et al, 2020), Evaluation of raster resolution , Spatial heterogeneity Model .…”

Section: Geoinformationmentioning

confidence: 99%

Raster-based Model for Mass Movement in Malang Regency, East Java, Indonesia.

Wibowo

Lavigne

Rifai

et al. 2020

J. Geoscience Eng. Environ. Technol.

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Strengthening geospatial technology is very important in order to support disaster mitigation strategy, to manage vulnerable communities and to protectcritical environments. The main challenge in identifying disaster characteristics such as mass movements is the lack of direct observation during the event because it is too dangerous for researchers. Geo-Information Technology as a product of Geographic Information Science can be used as a solution in order to model the characteristics of mass movements. The purpose of this study is focused on identifying landslide processes from point of view ofraster-based model. The method of thisresearch emphasizes dynamic landslide model derived from timeseries raster calculation using MassMov2D algorithm. The geographic database that was built for spatial modeling comes from pedogeomorphological and Remote Sensing survey outputs, especially topographic data, landforms and soil physical properties. The result shows that the relationship between pixels (neighborhood) is determined by the topology of the energy gradient line direction which allowsto transfer the value between each pixel.The movement of landslide material starts from the toe. This decreases the stability of the landslide material in the main body of the landslide and generate progressive erosion.The raster-based model can finally reconstruct and identify the stages of initiation, transport and deposition landslide material.

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