Page 167 - 《武汉大学学报(信息科学版)》2025年第6期
P. 167
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-