Landslide susceptibility zoning with five data models and performance comparison in Liangshan Prefecture, China

Frontiers·Jul 8

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Research is being conducted on landslide susceptibility zoning, with various studies exploring the use of different data models and techniques, including machine learning and hybrid approaches. These studies are being applied to different regions, including Liangshan Prefecture in China, as well as other locations in China and Algeria. The performance of these models is being compared and assessed to improve landslide susceptibility assessment and risk mapping.

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Research on the problem of spatial heterogeneity in row data and generalization capability for landslide susceptibility assessment using the physics-constrained U-net model

Nature·Apr 3

Remote sensing-based landslide prediction and risk assessment using a hybrid CNN–LSTM deep learning model

Nature·Mar 29

Integration of Physics-Based and Data-Driven Approaches for Landslide Susceptibility Assessment

Wiley Online Library·Jul 2

Comparative assessment of landslide susceptibility in Fugu town using machine learning models at multiple grid resolutions

Nature·Dec 13

Integrating InSAR-derived deformation into landslide susceptibility mapping in the Ninglang–Yongsheng expressway region, Yunnan, China

Frontiers·Apr 10

Optimizing machine learning and bagging-based hybrid models for landslide susceptibility mapping: a case study in Chenggu County, China

Nature·Nov 27

Performance comparison of post-earthquake disaster susceptibility assessment models based on GIS: a case study of the Lushan County in Ya’an City, China

Nature·Oct 24

Landslide susceptibility assessment using hybrid geospatial, frequency ratio, and AHP models in Souk Ahras province, Northeastern of Algeria

Nature·Dec 12

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