Landslide Susceptibility Assessment with Time-Series Insar Deformation Optimization for Non-Landslide Sample Selection Supported by Machine Learning

Author:

Wan Junhui,Xiao Haiping,Chen Lanlan,Wang Xiaobing,Weng Shanshan

Publisher

Elsevier BV

Reference26 articles.

1. Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin;T Zeng;Geosci. Front,2023

2. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and na�ve Bayes tree for landslide susceptibility modeling;W Chen;Sci. Total Environ,2018

3. Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya;A Merghadi;Int. J. Sediment Res,2019

4. Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador);S L Cobos-Mora;Geomorphology (Amst),2020

5. Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions;F Huang;Geosci. Front,2022

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