Page 167 - 《武汉大学学报(信息科学版)》2025年第6期
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第 50 卷第 6 期             徐胜华等:多尺度特征学习的轻量化滑坡易发性评价方法                                    1189


                    Journal for Science and Engineering, 2022, 47(6):   报, 2022, 30(3): 908-919.
                    7367-7385.                                       WANG Shibao, ZHUANG Jianqi, ZHENG Jia, et
               [16]  孙德亮, 陈丹璐, 密长林, 等 .  基于随机森林-特征                   al.   Landslide  Susceptibility  Evaluation  Based  on
                    递归消除模型的可解释性缓丘岭谷地貌滑坡易发                            Deep  Learning  Along  Kangding − Litang  Section  of
                    性 评 价[J].   地 质 力 学 学 报 ,  2023,  29(2):  202-   CZ  Railway[J].   Journal  of  Engineering  Geology,
                    219.                                             2022, 30(3): 908-919.
                    SUN  Deliang,  CHEN  Danlu,  MI  Changlin,  et  al.    [23]  LIU  R,  YANG  X,  XU  C,  et  al.   Comparative
                    Evaluation  of  Landslide  Susceptibility  in  the  Gentle   Study of Convolutional Neural Network and Conven‑
                    Hill-Valley  Areas  Based  on  the  Interpretable  Ran‑  tional Machine Learning Methods for Landslide Sus‑
                    dom  Forest-Recursive  Feature  Elimination  Model  ceptibility  Mapping[J].   Remote  Sensing,  2022,  14
                    [J].   Journal  of  Geomechanics,  2023,  29(2) :   (2): 321.
                    202-219.                                    [24]  YI Y N, ZHANG Z J, ZHANG W C, et al.  Land‑
               [17]  FANG Z C, WANG Y, DUAN G H, et al.  Land‑       slide  Susceptibility  Mapping  Using  Multiscale  Sam‑
                    slide  Susceptibility  Mapping  Using  Rotation  Forest   pling  Strategy  and  Convolutional  Neural  Network:
                    Ensemble  Technique  with  Different  Decision  Trees   A Case Study in Jiuzhaigou Region[J].  CATENA,
                    in the Three Gorges Reservoir Area, China[J].  Re‑  2020, 195: 104851.
                    mote Sensing, 2021, 13(2): 238.             [25]  WANG Z L, XU S H, LIU J P, et al.  A Combina‑
               [18]  ZHAO  Q  F ,  CHEN  W ,  PENG  C  H ,  et  al.    tion  of  Deep  Autoencoder  and  Multi-scale  Residual
                    Modeling  Landslide  Susceptibility  Using  an  Eviden‑  Network for Landslide Susceptibility Evaluation[J].
                    tial Belief Function-Based Multiclass Alternating De‑  Remote Sensing, 2023, 15(3): 653.
                    cision  Tree  and  Logistic  Model  Tree[J].   Environ‑  [26]  LIU T, CHEN T, NIU R Q, et al.  Landslide De‑
                    mental Earth Sciences, 2022, 81(15): 404.        tection  Mapping  Employing  CNN,  ResNet,  and
               [19]  郭天颂, 张菊清, 韩煜, 等 .  基于粒子群优化支持                    DenseNet  in  the  Three  Gorges  Reservoir,  China
                    向量机的延长县滑坡易发性评价[J].  地质科技情                       [J].   IEEE  Journal  of  Selected  Topics  in  Applied
                    报, 2019, 38(3): 236-243.                         Earth Observations and Remote Sensing, 2021, 14:
                    GUO Tiansong, ZHANG Juqing, HAN Yu, et al.       11417-11428.
                    Evaluation  of  Landslide  Susceptibility  in  Yanchang   [27]  GE Y F, LIU G, TANG H M, et al.  Comparative
                    County Based on Particle Swarm Optimization Sup‑  Analysis of Five Convolutional Neural Networks for
                    port  Vector  Machine[J].   Geological  Science  and   Landslide Susceptibility Assessment[J].  Bulletin of
                    Technology Information, 2019, 38(3): 236-243.    Engineering  Geology  and  the  Environment,  2023,
               [20]  徐胜华, 刘纪平, 王想红, 等 .  熵指数融入支持向                    82(10): 377.
                    量机的滑坡灾害易发性评价方法: 以陕西省为例                      [28]  马欣蕊, 徐胜华, 王琢璐, 等 .  融合自编码与密集
                    [J].  武 汉 大 学 学 报(信 息 科 学 版), 2020, 45(8):       残差网络的滑坡易发性评价[J].  测绘科学, 2023,
                    1214-1222.                                       48(7): 146-154.
                    XU  Shenghua,  LIU  Jiping,  WANG  Xianghong,  et   MA Xinrui, XU Shenghua, WANG Zhuolu, et al.
                    al.   Landslide  Susceptibility  Assessment  Method  In‑  A  Combination  of  Autoencoder  and  Dense  Residual
                    corporating Index of Entropy Based on Support Vec‑  Network for Landslide Susceptibility Evaluation[J].
                    tor Machine: A Case Study of Shaanxi Province[J].    Science  of  Surveying  and  Mapping,  2023,  48(7):
                    Geomatics  and  Information  Science  of  Wuhan  Uni‑  146-154.
                    versity, 2020, 45(8): 1214-1222.            [29]  蔡超 .  基于多源数据与尺度分割的滑坡易发性评价
               [21]  林荣福 .  基于优化支持向量机模型的滑坡易发性评                       方法研究: 以上犹县为例[D].  赣州: 江西理工大
                    价: 以陕西省商洛市为例[D].  阜新: 辽宁工程技                      学, 2021.
                    术大学, 2021.                                       CAI  Chao.   Landslide  Susceptibility  Evaluation
                    LIN  Rongfu.   Landslide  Susceptibility  Evaluation   Method Based on Multi-source Data and Scale Seg‑
                    Based on Optimized Support Vector Machine Model   mentation  −  The  Example  of  Shangyou  Country
                    − Taking  Shangluo  City  of  Shaanxi  Province  as  an   [D].   Ganzhou:  Jiangxi  University  of  Science  and
                    Example[D].   Fuxin:  Liaoning  Technical  Universi‑  Technology, 2021.
                    ty, 2021.                                   [30]  HOWARD A G, ZHU M L, CHEN B, et al.  Mo‑
               [22]  王世宝, 庄建琦, 郑佳, 等 .  基于深度学习的 CZ 铁                 bileNets:  Efficient  Convolutional  Neural  Networks
                    路康定—理塘段滑坡易发性评价[J].  工程地质学                        for  Mobile  Vision  Applications[EB/OL].  [2018-
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