Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms

Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms

Landslides are recognized as high-impact natural hazards in different regions around the world; therefore, they are extensively researched by experts. Landslide inventories are essential to identify areas that are likely to be affected in the future, thereby enabling interventions to prevent loss of...

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Auteurs principaux: Rocío N. Ramos-Bernal, René Vázquez-Jiménez, Claudia A. Cantú-Ramírez, Antonio Alarcón-Paredes, Gustavo A. Alonso-Silverio, Adrián G. Bruzón, Fátima Arrogante-Funes, Fidel Martín-González, Carlos J. Novillo, Patricia Arrogante-Funes
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
landslides
continuous change maps
conditioning factors
machine learning
Science
Q
Accès en ligne:https://doaj.org/article/f1611f2935aa421bb25b372fc4a90ee7
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