Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China
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DOI: 10.1007/s11069-020-04371-4
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- Hu, Michael Y. & Tsoukalas, Christos, 2003. "Explaining consumer choice through neural networks: The stacked generalization approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 650-660, May.
- Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
- Taskin Kavzoglu & Emrehan Kutlug Sahin & Ismail Colkesen, 2015. "An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(1), pages 471-496, March.
- Guilherme Garcia Oliveira & Luis Fernando Chimelo Ruiz & Laurindo Antonio Guasselli & Claus Haetinger, 2019. "Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 1049-1073, November.
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Cited by:
- Siti Norsakinah Selamat & Nuriah Abd Majid & Aizat Mohd Taib, 2023. "A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-21, January.
- Jiaxuan Huang & Weichao Du & Mowen Xie, 2023. "Numerical Modeling of Kinetic Features and Stability Analysis of Jinpingzi Landslide," Land, MDPI, vol. 12(3), pages 1-17, March.
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Keywords
Landslides; Artificial neural network; Decision tree; Bagging; Boosting; Stacking;All these keywords.
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