Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China
By Wei Chen, Huichan Chai, Zhou Zhao, Qiqing Wang, Haoyuan Hong. Environmental Earth Sciences, 2016
111 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law
View Paper: https://www.semanticscholar.org/paper/4d1541fc6eac3d1fbe304141169ad80daa074f72
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
- D. Tien Bui
- Haoyuan Hong
- Biswajeet Pradhan
- Jianquan Ma
- Zhao Duan
- Xiaoshen Xie
- Jiale Wang
- Binh Thai Pham
In Management, Monitoring, Policy and Law
- D. Tien Bui
- Haoyuan Hong
- Biswajeet Pradhan
- Jianquan Ma
- Zhao Duan
- Xiaoshen Xie
- Jiale Wang
- Binh Thai Pham
Citations in the corpus
- A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility (2017, methodology): “Globally, several statistical models combined with GIS have been used for landslide susceptibility assessment, such as statistical index (Chen et al., 2016a; Constantin et al., 2011; Nasiri Aghdam et al., 2016), index of entropy (IOE) (Constantin et al., 2011; Devkota et al., 2013; Youssef et al., 2”