Improving Voting Feature Intervals for Spatial Prediction of Landslides
In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid...
Main Authors: | Binh Thai Pham, Tran Van Phong, Mohammadtaghi Avand, Nadhir Al-Ansari, Sushant K. Singh, Hiep Van Le, Indra Prakash |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2020-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/4310791 |
Similar Items
-
Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin
by: Nguyen Van Dung, et al.
Published: (2021-01-01) -
Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India
by: Trinh Quoc Ngo, et al.
Published: (2021-01-01) -
GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment
by: Binh Thai Pham, et al.
Published: (2020-03-01) -
GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India
by: Trung-Hieu Tran, et al.
Published: (2021-01-01) -
Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
by: Phong Tung Nguyen, et al.
Published: (2020-04-01)