Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping
Ensemble learning methods have been widely used due to their remarkable generalized performance, but their potential in landslide spatial prediction application is not fully studied. To take full advantage of ensemble learning techniques, the classification and regression tree classifier and four tr...
Main Authors: | Jiahui Song, Yi Wang, Zhice Fang, Ling Peng, Haoyuan Hong |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9157927/ |
Similar Items
-
An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru
by: Chandan Kumar, et al.
Published: (2023-02-01) -
Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy
by: Taorui Zeng, et al.
Published: (2023-11-01) -
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping
by: Amirhosein Mosavi, et al.
Published: (2020-01-01) -
Assessing landslide susceptibility based on hybrid Best-first decision tree with ensemble learning model
by: Haoyuan Hong
Published: (2023-03-01) -
Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique
by: Chao Zhou, et al.
Published: (2024-01-01)