A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)
By Binh Thai Pham, Biswajeet Pradhan, D. Tien Bui, Indra Prakash, M. Dholakia. Environmental Modelling & Software, 2016
499 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management, Monitoring, Policy and Law, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/4ff10800d327eaef06558c81db415575b2f7d550
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kunlong Yin
- Chao Zhou
- Hamid Reza Pourghasemi
- Yuanyao Li
- Ying Cao
- Bayes Ahmed
- Filippo Catani
- Saro Lee
In Management, Monitoring, Policy and Law
- Hamid Reza Pourghasemi
- Saro Lee
- Chang-Wook Lee
- Filippo Catani
- Sungjae Park
- Amiya Gayen
- Kunlong Yin
- Chao Zhou
In Mechanical Engineering
- Hamid Reza Pourghasemi
- Filippo Catani
- Chang-Wook Lee
- Kunlong Yin
- Chao Zhou
- Yuanyao Li
- Ying Cao
- Bayes Ahmed
Citations in the corpus
- Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, background): “Statistical based models are required large amounts of data, whereas machine-learning algorithms based models can effectively overcome the limitation of data dependency [9,37].”
- Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China (2018, methodology): “…as reported in previous literatures, the SVM model 421 M AN US CR IP T AC CE PT ED ACCEPTED MANUSCRIPTachieved accurate prediction in almost all the cases (Pradhan, 2013; Peng et al., 2014; Pham et al., 2016a; Lee et 422 al., 2017), which stated that the performance of SVM has a strong robustness.”