Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data
Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the...
Saved in:
Main Authors: | Kalantar, Bahareh, Ueda, Naonori, Saeidi, Vahideh, Ahmadi, Kourosh, Abdul Halin, Alfian, Shabani, Farzin |
---|---|
Format: | Article |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2020
|
Online Access: | http://psasir.upm.edu.my/id/eprint/89546/1/Landslide%20susceptibility%20mapping.pdf http://psasir.upm.edu.my/id/eprint/89546/ https://www.mdpi.com/2072-4292/12/11/1737 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Conditioning factors determination for landslide susceptibility mapping using support vector machine learning
by: Kalantar, Bahareh, et al.
Published: (2019) -
On the use of XAI for CNN model interpretation: a remote sensing case study
by: Moradi, Loghman, et al.
Published: (2022) -
Fire-net: a deep learning framework for active forest fire detection
by: Seydi, Seyd Teymoor, et al.
Published: (2022) -
Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning
by: Solihin M.I., et al.
Published: (2024) -
Optimized conditioning factors using machine learning techniques for groundwater potential mapping
by: Kalantar, Bahareh, et al.
Published: (2019)