DATA IMBALANCE IN LANDSLIDE SUSCEPTIBILITY ZONATION: UNDER-SAMPLING FOR CLASS-IMBALANCE LEARNING

8 citations as recorded by crossref.

1. Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas S. Gupta & D. Shukla https://doi.org/10.1007/s10346-022-01998-1
2. A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN) H. Al-Najjar et al. https://doi.org/10.3390/rs13194011
3. Examining the role of class imbalance handling strategies in predicting earthquake-induced landslide-prone regions Q. Pham et al. https://doi.org/10.1016/j.asoc.2023.110429
4. Prediction of Postcoronary Artery Bypass Grafting Atrial Fibrillation: POAFRiskScore Tool A. Arslan et al. https://doi.org/10.1055/s-0041-1736245
5. Enhancing the prediction of landslide susceptibility mapping by using hybrid machine learning models with imbalanced data V. Le et al. https://doi.org/10.1007/s40808-026-02845-1
6. Assessment of landslide susceptibility based on Bayes-optimized RUSBoost model—taking the three Gorges Reservoir area as an example R. Wu et al. https://doi.org/10.1007/s10064-025-04436-3
7. To explore the optimal solution of different mapping units and classifiers and their application in the susceptibility evaluation of slope geological disasters S. Zhang et al. https://doi.org/10.1016/j.ecolind.2024.112073
8. Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks Z. Chang et al. https://doi.org/10.3390/s25010001