Landslide susceptibility assessment is the problem of determining the likelihood of a landslide to occur in a particular area based on the geological and morphological properties of the area. In this study we propose a method wherein skyline operator is used to model landslides and majority voting is used to assess landslide susceptibility. Experiments conducted on a real life data set show that the proposed method achieves 83.07% classification accuracy and is superior over logistic regression, support vector machine and neural network based approaches and achieves similar results when compared to a decision trees-based model.
Primary Language | English |
---|---|
Subjects | Computer Software, Environmental Sciences |
Journal Section | Research Articles |
Authors | |
Publication Date | October 1, 2019 |
Submission Date | November 7, 2018 |
Acceptance Date | March 20, 2019 |
Published in Issue | Year 2019 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.