Modeling of landslides susceptibility prediction using deep belief networks with optimized learning rate control-Reference-Cited by-同舟云学术

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Modeling of landslides susceptibility prediction using deep belief networks with optimized learning rate control

Published:2024-02-21 Issue:1 Volume:39 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Liu Qiang¹²,Norbu Namkha²

Affiliation:

1. Shaanxi Provincial Land Engineering Construction Group, Shaanxi Dijian Land Comprehensive Development Co., Ltd., Xi’an, China

2. Shaanxi Provincial Land Engineering Construction Group, Shaanxi Dijian Mining Development & Environment Protection Co., Ltd., Xi’an, China

Funder

National Science Foundation of Shaanxi Provence of China

National Natural Science Foundation of China

Innovation Capability Support Program of Shaanxi Province

Key Research and Development Program of Shaanxi Province

Inner scientific research project of Shaanxi Land Engineering Construction Group

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2322060

Reference32 articles.

1. Deep learning-based landslide susceptibility mapping

2. Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

3. Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas

4. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment

5. Using Deep Learning to Formulate the Landslide Rainfall Threshold of the Potential Large-Scale Landslide

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