TITLE:
Remote Sensing Landslide Monitoring Based on Machine Learning Method
AUTHORS:
Zhen Chen, Yiyang Zheng
KEYWORDS:
Landslide, Evaluation Index, Random Forest, Geological Disaster
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.11 No.10,
October
20,
2023
ABSTRACT: The susceptibility evaluation of landslides has become one of the key
environmental issues that people are
concerned about. This study took the land-slides in Xishuangbanna, Yunnan Province as the study
object, and selected 10 evaluation factors
such as digital elevation model (DEM), slope aspect, precipitation, land
use, water system, roads, population density, lithology, faults, and NDVI.
Different machine learning methods were compared and studied, and the ROC (receiver
operating characteristics) curve verification revealed that the accuracy of the
random forest evaluation model was high. In the prediction and evaluation of
the susceptibility of landslides, five risk levels were divided. After the
superimposed analysis, 87.26% of the disaster points fell in the first and
second susceptibility areas. The spot analysis found that the distribution of
hot spots is consistent with the distribution of disaster spots. In a word, the results of this study can provide better
technical support for the evaluation and early warning of landslides in Southwest China.