Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional…
By Binh Thai Pham, D. Tien Bui, Hamid Reza Pourghasemi, P. Indra, M. Dholakia. Theoretical and Applied Climatology, 2017
364 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/53e04e3db85f13ff829d2dd482f7fb2b7665208e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
- Binh Thai Pham
- Indra Prakash
- D. Tien Bui
- Khabat Khosravi
- M. Dholakia
- Biswajeet Pradhan
- Hamid Reza Pourghasemi
- Himan Shahabi
In Management, Monitoring, Policy and Law
- Binh Thai Pham
- D. Tien Bui
- Indra Prakash
- Khabat Khosravi
- M. Dholakia
- Biswajeet Pradhan
- Hamid Reza Pourghasemi
- Himan Shahabi
In Mechanical Engineering
- D. Tien Bui
- Binh Thai Pham
- Indra Prakash
- Khabat Khosravi
- Biswajeet Pradhan
- M. Dholakia
- Hamid Reza Pourghasemi
- Himan Shahabi
Citations in the corpus
- Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India (2017, methodology): “The Rainfall creates instability in rock mass/soil mass, and it is one of the most common causes of landslides (Pham et al. 2015a; Tien Bui et al. 2016a).”
- A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, methodology): “NB is also based on Bayes' theorem (Pham et al., 2015b) that is a statistical approach using a conditional independence assumption that all attributes are independent with given output class (Soria et al., 2011).”