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The aim is to develop a better method for assessing landslide susceptibility influenced by geology and tectonics.

Sampling effects on machine-learning performance and tectonic controls on landslide susceptibility: insights from the Adra River basin (SE Spain)

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Demonstrates improved landslide susceptibility assessment in a diverse terrain, indicating significant geological influences.

The aim is to develop a better method for assessing landslide susceptibility influenced by geology and tectonics.
Compared centroid-based and proportional stratified sampling methods.
Tested seven machine-learning models and one mean-ensemble configuration.
Reduced landslide conditioning factors from 19 to 14 through feature selection.
Evaluated model performance using predictive accuracy and Degree of Fit metrics.
The GWLR-Mean-PSS ensemble achieved a high predictive accuracy (AUC = 0.960; Accuracy = 89%).
Demonstrated low spatial bias and a Degree of Fit of 96.69%.
Susceptibility patterns correspond closely with the basin’s geological and tectonic features.

Boussouf et al. (2026) studied this question.

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View Full Paper

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Authors

Demonstrates improved landslide susceptibility assessment in a diverse terrain, indicating significant geological influences.

The aim is to develop a better method for assessing landslide susceptibility influenced by geology and tectonics.
Compared centroid-based and proportional stratified sampling methods.
Tested seven machine-learning models and one mean-ensemble configuration.
Reduced landslide conditioning factors from 19 to 14 through feature selection.
Evaluated model performance using predictive accuracy and Degree of Fit metrics.
The GWLR-Mean-PSS ensemble achieved a high predictive accuracy (AUC = 0.960; Accuracy = 89%).
Demonstrated low spatial bias and a Degree of Fit of 96.69%.
Susceptibility patterns correspond closely with the basin’s geological and tectonic features.

Boussouf et al. (2026) studied this question.

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