Application of convolutional neural network fused with machine learning modeling framework for geospatial comparative analysis of landslide susceptibility
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DOI: 10.1007/s11069-022-05326-7
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Cited by:
- Yufeng He & Mingtao Ding & Hao Zheng & Zemin Gao & Tao Huang & Yu Duan & Xingjie Cui & Siyuan Luo, 2023. "Integrating development inhomogeneity into geological disasters risk assessment framework in mountainous areas: a case study in Lushan–Baoxing counties, Southwestern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 3203-3229, July.
- Fan Liu & Yahong Deng & Tianyu Zhang & Faqiao Qian & Nan Yang & Hongquan Teng & Wei Shi & Xue Han, 2024. "Landslide Distribution and Development Characteristics in the Beiluo River Basin," Land, MDPI, vol. 13(7), pages 1-28, July.
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Keywords
Landslide susceptibility; Hybrid convolutional neural networks; Machine learning models; Wenchuan County; Southwest China;All these keywords.
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