Introduction to Determining Landslide

This paper presents an application of PS-InSAR method for determining landslide displacement velocity in Van Yen district, Yen Bai province, Vietnam.

Numerical simulation also demonstrated that residual soil strength is an important factor in determining landslide runoff and deposition.

A rapid assessment of landslide risk level is carried out based on deep learning to speed up the process of determining landslide risk level.

It conducted to the neuro fuzzy method in determining landslide prone areas.

The dendrochronological analysis of tree growth disturbances (eccentric growth and compression wood) is a promising approach for determining landslide hazards in forested mountain areas and can be applied in spatial management.

The ratio (Ω) between landslide erosion and uplift is most sensitive to parameters determining landslide volumes (particularly earthquake magnitude M_w, seismic energy source depth, and failure susceptibility, as well as the seismological factor responsible for triggering landslides), and is moderately sensitive to the effective elastic thickness of lithosphere, T_e.

These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.

Landslide maps are also crucial for determining landslide susceptibility and risk.

But geospatial technique provides capability of reaching every part of the inaccessible area, identifying all types of landslides and determining landslide prone zone on the basis of major causative factors.