A New Approach Based on Balancing Composite Motion Optimization and Deep Neural Networks for Spatial Prediction of Landslides at Tropical Cyclone Areas-Reference-Cited by-同舟云学术

A New Approach Based on Balancing Composite Motion Optimization and Deep Neural Networks for Spatial Prediction of Landslides at Tropical Cyclone Areas

Published:2023 Issue: Volume:11 Page:69495-69511
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Tuan Tran Anh¹^ORCID,Pha Phan Dong¹,Tam Tran Thi²,Bui Dieu Tien³^ORCID

Affiliation:

1. Institute of Marine Geology and Geophysics, Vietnam Academy of Science and Technology, Cau Giay, Hanoi, Vietnam

2. Center for Agricultural Meteorology, Vietnam Institute of Meteorology, Hydrology and Climate Change, Dong Da, Hanoi, Vietnam

3. Department of Business and IT, GIS Group, University of South-Eastern Norway, Bø i Telemark, Norway

Funder

Vietnam Academy of Science and Technology

Department of Business and IT, University of South-Eastern Norway, Bø i Telemark

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10171369.pdf?arnumber=10171369

Reference71 articles.

1. Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

2. A strategy for GIS-based 3-D slope stability modelling over large areas

3. Space–time prediction of rainfall-induced shallow landslides through a combined probabilistic/deterministic approach, optimized for initial water table conditions

4. A critical review on landslide susceptibility zonation: recent trends, techniques, and practices in Indian Himalaya

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