Articles | Volume 24, issue 6
https://doi.org/10.5194/nhess-24-1913-2024
https://doi.org/10.5194/nhess-24-1913-2024
Research article
 | 
06 Jun 2024
Research article |  | 06 Jun 2024

Addressing class imbalance in soil movement predictions

Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt

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Short summary
Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
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