Addressing class imbalance in soil movement predictions
Abstract 70: 30 ratio of training and testing data. To tackle the class imbalance problem, various oversampling techniques, including the synthetic minority oversampling technique (SMOTE), K K -means SMOTE performed the best in testing, with an accuracy, precision, and recall rate of 0.995, 0.995, and 0.995, respectively, and an F1 score of 0.995. Additionally, models without oversampling exhibited poor performance in training and testing, highlighting the importance of incorporating oversampling techniques to enhance predictive capabilities.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Addressing class imbalance in soil movement predictions ; volume:24 ; number:6 ; year:2024 ; pages:1913-1928 ; extent:16
Natural hazards and earth system sciences ; 24, Heft 6 (2024), 1913-1928 (gesamt 16)
- Urheber
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Kumar, Praveen
Priyanka, Priyanka
Uday, Kala Venkata
Dutt, Varun
- DOI
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10.5194/nhess-24-1913-2024
- URN
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urn:nbn:de:101:1-2408051426115.631373882839
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 11:03 MESZ
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Beteiligte
- Kumar, Praveen
- Priyanka, Priyanka
- Uday, Kala Venkata
- Dutt, Varun
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