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
2. A Hybrid Neural Network-Based Model for Landslide Susceptibility Mapping L. Chen et al.
3. A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN) H. Al-Najjar et al.
4. Examining the role of class imbalance handling strategies in predicting earthquake-induced landslide-prone regions Q. Pham et al.
5. Prediction of Postcoronary Artery Bypass Grafting Atrial Fibrillation: POAFRiskScore Tool A. Arslan et al.
6. Assessment of landslide susceptibility based on Bayes-optimized RUSBoost model—taking the three Gorges Reservoir area as an example R. Wu et al.
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.
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