Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment

Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment

To address the errors of negative samples in landslide susceptibility modeling and traditional methods in exploring the regularities hidden in the evaluation factors, this paper proposes a stacking one- and three-dimensional Convolutional Neural Network (Stacking-1D-3D-CNN) landslide susceptibility...

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Bibliographic Details
Main Authors: Yueyue Wang, Xueling Wu, Kun Zhou, Guo Lin, Bo Peng, Zhice Fang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-01-01
Series:Geo-spatial Information Science
Subjects:
Landslide susceptibility
relative frequency ratio (RFR)
certainty factor method (CFM)
stacking
convolutional neural network (CNN)
Online Access:https://www.tandfonline.com/doi/10.1080/10095020.2024.2443483
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https://www.tandfonline.com/doi/10.1080/10095020.2024.2443483

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