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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods
A‐Xing Zhu, Miao Yamin, Junzhi Liu, et al.
CATENA (2019) Vol. 183, pp. 104188-104188
Closed Access | Times Cited: 126

Showing 1-25 of 126 citing articles:

Comparative study of landslide susceptibility mapping with different recurrent neural networks
Yi Wang, Zhice Fang, Mao Wang, et al.
Computers & Geosciences (2020) Vol. 138, pp. 104445-104445
Closed Access | Times Cited: 230
GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models
Wei Chen, Yang Li
CATENA (2020) Vol. 195, pp. 104777-104777
Closed Access | Times Cited: 204
Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region
Yaning Yi, Zhijie Zhang, Wanchang Zhang, et al.
CATENA (2020) Vol. 195, pp. 104851-104851
Closed Access | Times Cited: 196
Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble
Haoyuan Hong, Junzhi Liu, A‐Xing Zhu
The Science of The Total Environment (2020) Vol. 718, pp. 137231-137231
Closed Access | Times Cited: 176
Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area
Viet‐Ha Nhu, Nhat‐Duc Hoang, Hieu Nguyen, et al.
CATENA (2020) Vol. 188, pp. 104458-104458
Closed Access | Times Cited: 140
Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples
Can Yang, Leilei Liu, Faming Huang, et al.
Gondwana Research (2022) Vol. 123, pp. 198-216
Closed Access | Times Cited: 94
A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples
Deliang Sun, WU Xiao-qing, Haijia Wen, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 52
How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? — A catchment-scale case study from China
Zizheng Guo, Bixia Tian, Yuhang Zhu, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024) Vol. 16, Iss. 3, pp. 877-894
Open Access | Times Cited: 36
A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm
Shaoqiang Meng, Zhenming Shi, Gang Li, et al.
Computers and Geotechnics (2024) Vol. 167, pp. 106106-106106
Closed Access | Times Cited: 26
Effects of non-landslide sampling strategies on machine learning models in landslide susceptibility mapping
Tengfei Gu, Ping Duan, Mingguo Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 23
Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks
Husam A. H. Al-Najjar, Biswajeet Pradhan
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 625-637
Open Access | Times Cited: 139
Dynamic development of landslide susceptibility based on slope unit and deep neural networks
Hua Ye, Xianmin Wang, LI Yong-wei, et al.
Landslides (2020) Vol. 18, Iss. 1, pp. 281-302
Closed Access | Times Cited: 99
A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility
Viet-Hung Dang, Nhat‐Duc Hoang, Le-Mai-Duyen Nguyen, et al.
Forests (2020) Vol. 11, Iss. 1, pp. 118-118
Open Access | Times Cited: 78
How is the Third Law of Geography different?
A‐Xing Zhu, Matthew D. Turner
Annals of GIS (2022) Vol. 28, Iss. 1, pp. 57-67
Open Access | Times Cited: 66
Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies
Faming Huang, Siyu Tao, Deying Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4436-4436
Open Access | Times Cited: 51
Impact of Land Use/Land Cover Change on Landslide Susceptibility in Rangamati Municipality of Rangamati District, Bangladesh
Yasin Wahid Rabby, Yingkui Li, Joynal Abedin, et al.
ISPRS International Journal of Geo-Information (2022) Vol. 11, Iss. 2, pp. 89-89
Open Access | Times Cited: 46
Effectiveness of Newmark-based sampling strategy for coseismic landslide susceptibility mapping using deep learning, support vector machine, and logistic regression
Chuanjie Xi, Mei Han, Xiewen Hu, et al.
Bulletin of Engineering Geology and the Environment (2022) Vol. 81, Iss. 5
Closed Access | Times Cited: 42
Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides
Qiang Liu, Aiping Tang, Delong Huang
CATENA (2023) Vol. 227, pp. 107109-107109
Closed Access | Times Cited: 35
Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation
Deliang Sun, D C Chen, Jialan Zhang, et al.
Land (2023) Vol. 12, Iss. 5, pp. 1018-1018
Open Access | Times Cited: 35
Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales
Xin Wei, Lulu Zhang, Paolo Gardoni, et al.
Acta Geotechnica (2023) Vol. 18, Iss. 8, pp. 4453-4476
Closed Access | Times Cited: 33
An objective absence data sampling method for landslide susceptibility mapping
Yasin Wahid Rabby, Yingkui Li, Haileab Hilafu
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 26
Geographically weighted neural network considering spatial heterogeneity for landslide susceptibility mapping: A case study of Yichang City, China
Zhongguo Zhao, Zhangyan Xu, Chuli Hu, et al.
CATENA (2023) Vol. 234, pp. 107590-107590
Open Access | Times Cited: 23
Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China
Ruiqi Zhang, Lele Zhang, Zhice Fang, et al.
Remote Sensing (2024) Vol. 16, Iss. 13, pp. 2394-2394
Open Access | Times Cited: 11
Influences of non-landslide sampling strategies on landslide susceptibility mapping: a case of Tianshui city, Northwest of China
Chaoying Ke, Ping Sun, Shuai Zhang, et al.
Bulletin of Engineering Geology and the Environment (2025) Vol. 84, Iss. 3
Closed Access | Times Cited: 1
Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks
Luísa Vieira Lucchese, Guilherme Garcia de Oliveira, Olavo Corrêa Pedrollo
CATENA (2020) Vol. 198, pp. 105067-105067
Closed Access | Times Cited: 70
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