Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region

Abstract

Susceptibility to landslides in mountain areas results from the interaction of various factors related to relief formation and soil development. The assessment of landslide susceptibility has generally taken into account individual events, or it has been aimed at establishing relationships between landslide-inventory maps and maps of environmental factors, without considering that such relationships can change in space and time. In this work, temporal and space changes in landslides were analysed in six different combinations of date and geomorphological conditions, including two different geological units, in a mountainous area in the north-centre of Venezuela, in northern South America. Landslide inventories from different years were compared with a number of environmental factors by means of logistic regression analysis. The resulting equations predicted landslide susceptibility from a range of geomorphometric parameters and a vegetation index, with diverse accuracy, in the study area. The variation of the obtained models and their prediction accuracy between geological units and dates suggests that the complexity of the landslide processes and their explanatory factors changed over space and time in the studied area. This calls into question the use of a single model to evaluate landslide susceptibility over large regions.

Assessment of Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with Kernel Logistic Regression Model

“…Vegetation plays a positive role in the stability of landslides and can improve the shear strength of the soil, while increasing the stability of the slope [ 58 ]. According to the observations of extensive field investigation, the more vegetation there is, the lower the number of landslides.…”

Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China

“…For the three LR models, by using new landslides as validation data, it is found that using updated predisposing factors (2020) significantly improves the model performance compared to using the predisposing factors from 2010. This is primarily due to the dramatic LUC and urbanization, which also impact the fundamental geological and environmental conditions, such as the NDVI, land use, and traffic conditions [30,76,77]. Meanwhile, the models exhibit their best performance when employing time-varying predisposing factors.…”