Landslide identification using machine learning
Landslide identification is critical for risk assessment and mitigation. This paper proposes a novel machine-learning and deep-learning method to identify natural-terrain landslides using integrated geodatabases. First, landslide-related data are compiled, including topographic data, geological data...
Main Authors: | Haojie Wang, Limin Zhang, Kesheng Yin, Hongyu Luo, Jinhui Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-01-01
|
Series: | Geoscience Frontiers |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987120300542 |
Similar Items
-
Optimization of the landslide identification method based on a dual attention mechanism
by: Qi Wu, et al.
Published: (2022-03-01) -
Landslide risk assessment in Nanping City based on artificial neural networks model
by: Shuiman CHEN, et al.
Published: (2022-04-01) -
Landslide Susceptibility Modeling Using a Deep Random Neural Network
by: Cheng Huang, et al.
Published: (2022-12-01) -
Landslide Susceptibility Mapping by Fusing Convolutional Neural Networks and Vision Transformer
by: Shuai Bao, et al.
Published: (2022-12-01) -
A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction
by: Qiyuan Yang, et al.
Published: (2024-03-01)