Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geotechnical Engineering and Engineering Geology
Link
https://link.springer.com/content/pdf/10.1007/s10346-022-01998-1.pdf
Reference91 articles.
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3. Alkhasawneh MS, Tay LT (2018) A hybrid intelligent system integrating the cascade forward neural network with Elman neural network. Arab J Sci Eng 43:6737–6749. https://doi.org/10.1007/s13369-017-2833-3
4. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble decision tree-based chi-squared automatic interaction detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11:1063–1078. https://doi.org/10.1007/s10346-014-0466-0
5. Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25:559–572. https://doi.org/10.1080/0143116031000156819
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