Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms
Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karis...
Main Authors: | Ananta Man Singh Pradhan, Yun-Tae Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | ISPRS International Journal of Geo-Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2220-9964/9/10/569 |
Similar Items
-
Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China
by: Guangzhi Rong, et al.
Published: (2020-11-01) -
Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree
by: Hyun-Joo Oh, et al.
Published: (2017-09-01) -
Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
by: Prima Riza Kadavi, et al.
Published: (2018-08-01) -
Mapping landslide susceptibility and types using Random Forest
by: Khaled Taalab, et al.
Published: (2018-04-01) -
Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
by: Dieu Tien Bui, et al.
Published: (2019-04-01)