Methods for Evaluating Debris Flow Susceptibility Based on OOD Generalization Verification and Deep Fully Connected Neural Networks

被引：0
|
作者：
Guo, Pengning [1 ]
Xing, Huige [1 ]
Li, Congjiang [2 ,3 ]
Wu, Yuxin [1 ]
Li, Haibo [2 ,3 ]
机构：
[1] College of Architecture and Environment, Sichuan Univ., Chengdu,610065, China
[2] College of Water Resource & Hydropower, Sichuan Univ., Chengdu,610065, China
[3] State Key Lab. of Hydraulics and Mountain River Eng., Sichuan Univ., Chengdu,610065, China
来源：
Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences | 2024年 / 56卷 / 04期
关键词：
Bayesian networks - Debris - Decision trees - Deep neural networks - Disaster prevention - Forecasting - Iterative methods - Site selection;
D O I：
10.15961/j.jsuese.202201138
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：182 / 193
相关论文
共 50 条
  • [1] Generalization in fully-connected neural networks for time series forecasting
    Borovykh, Anastasia
    Oosterlee, Cornelis W.
    Bohte, Sander M.
    JOURNAL OF COMPUTATIONAL SCIENCE, 2019, 36
  • [2] Estimation of a regression function on a manifold by fully connected deep neural networks
    Kohler, Michael
    Langer, Sophie
    Reif, Ulrich
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2023, 222 : 160 - 181
  • [3] Deep Vein: Novel Finger Vein Verification Methods Based on Deep Convolutional Neural Networks
    Huang, Houjun
    Liu, Shilei
    Zheng, He
    Ni, Liao
    Zhang, Yi
    Li, Wenxin
    2017 IEEE INTERNATIONAL CONFERENCE ON IDENTITY, SECURITY AND BEHAVIOR ANALYSIS (ISBA), 2017,
  • [4] Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks
    Tang, Rui-Xuan
    Kulatilake, Pinnaduwa H. S. W.
    Yan, E-Chuan
    Cai, Jing-Sen
    BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, 2020, 79 (05) : 2235 - 2254
  • [5] Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks
    Rui-Xuan Tang
    Pinnaduwa H. S. W. Kulatilake
    E-Chuan Yan
    Jing-Sen Cai
    Bulletin of Engineering Geology and the Environment, 2020, 79 : 2235 - 2254
  • [6] Compressing fully connected layers of deep neural networks using permuted features
    Nagaraju, Dara
    Chandrachoodan, Nitin
    IET COMPUTERS AND DIGITAL TECHNIQUES, 2023, 17 (3-4): : 149 - 161
  • [7] Inferring Skin Lesion Segmentation With Fully Connected CRFs Based on Multiple Deep Convolutional Neural Networks
    Qiu, Yuming
    Cai, Jingyong
    Qin, Xiaolin
    Zhang, Ju
    IEEE ACCESS, 2020, 8 : 144246 - 144258
  • [8] Differentiable homotopy methods for gradually reinforcing the training of fully connected neural networks
    Li, Peixuan
    Li, Yuanbo
    NEUROCOMPUTING, 2024, 605
  • [9] A Design Flow Framework for Fully-Connected Neural Networks Rapid Prototyping
    Zompakis, Nikolaos
    Anagnostos, Dimitrios
    Koliogeorgi, Konstantina
    Zervakis, Georgios
    Siozios, Kostas
    INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS (COINS), 2019, : 44 - 49
  • [10] Partial Label Learning Based on Fully Connected Deep Neural Network
    Li H.
    Wu L.
    He J.
    Zheng R.
    Zhou Y.
    Qiao S.
    International Journal of Circuits, Systems and Signal Processing, 2022, 16 : 287 - 297
12345