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Chao Zhou
Chao Zhou
China University of Geosciences & GFZ German Research Center for Geosciences
Verified email at cug.edu.cn - Homepage
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Cited by
Year
Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China
C Zhou, K Yin, Y Cao, B Ahmed, Y Li, F Catani, HR Pourghasemi
Computers & geosciences 112, 23-37, 2018
3172018
Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China
C Zhou, K Yin, Y Cao, B Ahmed
Engineering geology 204, 108-120, 2016
3062016
Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method
C Zhou, K Yin, Y Cao, E Intrieri, B Ahmed, F Catani
Landslides 15, 2211-2225, 2018
1512018
Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors
Y Cao, K Yin, DE Alexander, C Zhou
Landslides 13, 725-736, 2016
1452016
Landslide characterization applying sentinel-1 images and InSAR technique: The muyubao landslide in the three Gorges Reservoir Area, China
C Zhou, Y Cao, K Yin, Y Wang, X Shi, F Catani, B Ahmed
Remote Sensing 12 (20), 3385, 2020
732020
Developing a dynamic Web-GIS based landslide early warning system for the Chittagong Metropolitan Area, Bangladesh
B Ahmed, MS Rahman, R Islam, P Sammonds, C Zhou, K Uddin, ...
ISPRS international journal of geo-information 7 (12), 485, 2018
602018
Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area
C Zhou, Y Cao, X Hu, K Yin, Y Wang, F Catani
Landslides 19 (7), 1585-1597, 2022
542022
A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms
C Zhou, K Yin, Y Cao, B Ahmed, X Fu
Scientific reports 8 (1), 7287, 2018
502018
Establishment of landslide groundwater level prediction model based on GA-SVM and influencing factor analysis
Y Cao, K Yin, C Zhou, B Ahmed
Sensors 20 (3), 845, 2020
472020
Characteristic comparison of seepage-driven and buoyancy-driven landslides in Three Gorges Reservoir area, China
C Zhou, Y Cao, K Yin, E Intrieri, F Catani, L Wu
Engineering Geology 301, 106590, 2022
462022
Landslide susceptibility mapping combining information gain ratio and support vector machines: a case study from Wushan segment in the three gorges reservoir area, China
L Yu, Y Cao, C Zhou, Y Wang, Z Huo
Applied Sciences 9 (22), 4756, 2019
442019
Landslide hazard probability and risk assessment at the community level: A case of western Hubei, China
S Fu, L Chen, T Woldai, K Yin, L Gui, D Li, J Du, C Zhou, Y Xu, Z Lian
Natural Hazards and Earth System Sciences 20 (2), 581-601, 2020
432020
Retrieval of time series three-dimensional landslide surface displacements from multi-angular SAR observations
X Shi, L Zhang, C Zhou, M Li, M Liao
Landslides 15 (5), 1015-1027, 2018
372018
Landslide displacement prediction combining LSTM and SVR algorithms: A case study of Shengjibao Landslide from the Three Gorges Reservoir Area
H Jiang, Y Li, C Zhou, H Hong, T Glade, K Yin
Applied Sciences 10 (21), 7830, 2020
352020
INFLUENCING FACTOR ANALYSIS AND DISPLACEMENT PREDICTION IN RESERVOIR LANDSLIDES− A CASE STUDY OF THREE GORGES RESERVOIR (CHINA).
F Huang, K Yin, T He, C Zhou, J Zhang
Tehnicki vjesnik/Technical Gazette 23 (2), 2016
322016
Landslide susceptibility assessment by applying the coupling method of radial basis neural network and adaboost: A case study from the Three Gorges Reservoir area
C Zhou, KL Yin, Y Cao, Y Li
Earth Science 45 (6), 1865-1876, 2020
292020
混沌序列 WA-ELM 耦合模型在滑坡位移预测中的应用
周超, 殷坤龙, 黄发明
岩土力学 36 (9), 2674-2680, 2015
262015
Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction
Z Chao, YIN Kun-long, H Fa-ming
Rock and Soil Mechanics 36 (9), 2674-2680, 2015
262015
基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价
周超, 殷坤龙, 曹颖, 李远耀
地球科学 45 (6), 1865-1876, 2020
242020
Remote sensing of photovoltaic scenarios: Techniques, applications and future directions
Q Chen, X Li, Z Zhang, C Zhou, Z Guo, Z Liu, H Zhang
Applied Energy 333, 120579, 2023
232023
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