An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India
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DOI: 10.1007/s11069-022-05360-5
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- Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
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- Viet-Tien Nguyen & Trong Hien Tran & Ngoc Anh Ha & Van Liem Ngo & Al-Ansari Nadhir & Van Phong Tran & Huu Duy Nguyen & Malek M. A. & Ata Amini & Indra Prakash & Lanh Si Ho & Binh Thai Pham, 2019. "GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam," Sustainability, MDPI, vol. 11(24), pages 1-24, December.
- Jean Baptiste Nsengiyumva & Geping Luo & Lamek Nahayo & Xiaotao Huang & Peng Cai, 2018. "Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda," IJERPH, MDPI, vol. 15(2), pages 1-23, January.
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- Francisco Parra & Jaime González & Max Chacón & Mauricio Marín, 2023. "Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile," Sustainability, MDPI, vol. 15(24), pages 1-31, December.
- Sheela Bhuvanendran Bhagya & Anita Saji Sumi & Sankaran Balaji & Jean Homian Danumah & Romulus Costache & Ambujendran Rajaneesh & Ajayakumar Gokul & Chandini Padmanabhapanicker Chandrasenan & Renata P, 2023. "Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps," Land, MDPI, vol. 12(2), pages 1-29, February.
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Keywords
Landslide susceptibility; Rangit River watershed; Random forest tree; Machine learning algorithms; Ensemble models;All these keywords.
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