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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:

Landslide Susceptibility Assessment Model Construction Using Typical Machine Learning for the Three Gorges Reservoir Area in China
Junying Cheng, Xiaoai Dai, Zekun Wang, et al.
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 2257-2257
Open Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

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: 21
A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China
Xinyue Yuan, Chao Liu, Ruihua Nie, et al.
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3259-3259
Open Access | Times Cited: 41
Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models
Aishwarya Sinha, S. Nikhil, R. S. Ajin, et al.
Fire (2023) Vol. 6, Iss. 2, pp. 44-44
Open Access | Times Cited: 33
Landslide Susceptibility Mapping Using an LSTM Model with Feature-Selecting for the Yangtze River Basin in China
Peng Zuo, Wen Zhao, Wenjun Yan, et al.
Water (2025) Vol. 17, Iss. 2, pp. 167-167
Open Access | Times Cited: 1
Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, China
Yanrong Liu, Zhongqiu Meng, Lei Zhu, et al.
Sustainability (2023) Vol. 15, Iss. 3, pp. 1971-1971
Open Access | Times Cited: 22
Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China
Xiaoliang Dai, Yunqiang Zhu, Kai Sun, et al.
Remote Sensing (2023) Vol. 15, Iss. 6, pp. 1513-1513
Open Access | Times Cited: 20
Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models
Yingze Song, Degang Yang, Weicheng Wu, et al.
ISPRS International Journal of Geo-Information (2023) Vol. 12, Iss. 5, pp. 197-197
Open Access | Times Cited: 18
Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model
Fasheng Miao, Qiuyu Ruan, Yiping Wu, et al.
Remote Sensing (2023) Vol. 15, Iss. 22, pp. 5427-5427
Open Access | Times Cited: 18
Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results
Xianyu Yu, Huihui Chen
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods
Han Huang, Yongsheng Wang, Yamei Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4475-4475
Open Access | Times Cited: 27
Assessment of environmental geological disaster susceptibility under a multimodel comparison to aid in the sustainable development of the regional economy
Cui Wang, Xuedong Wang, Heyong Zhang, et al.
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 3, pp. 6573-6591
Closed Access | Times Cited: 26
Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China
Wancai He, Guoping Chen, Junsan Zhao, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2549-2549
Open Access | Times Cited: 15
Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region
Mohib Ullah, Bingzhe Tang, Wenchao Huangfu, et al.
Land (2024) Vol. 13, Iss. 7, pp. 1011-1011
Open Access | Times Cited: 5
A novel landslide susceptibility prediction framework based on contrastive loss
Shubing Ouyang, Weitao Chen, Hangyuan Liu, et al.
GIScience & Remote Sensing (2024) Vol. 61, Iss. 1
Open Access | Times Cited: 4
Debris flow susceptibility assessment based on boosting ensemble learning techniques: a case study in the Tumen River basin, China
Zelu Chen, Hechun Quan, Ri Jin, et al.
Stochastic Environmental Research and Risk Assessment (2024) Vol. 38, Iss. 6, pp. 2359-2382
Closed Access | Times Cited: 4
Integrating dynamic factors for predicting future landslide susceptibility
Suraj Lamichhane, Arhat Ratna Kansakar, Nirajan Devkota, et al.
Environmental Earth Sciences (2025) Vol. 84, Iss. 3
Closed Access
Dynamic Landslide Susceptibility Mapping on Time-Series InSAR and Explainable Machine Learning: A Case Study at Wushan in the Three Gorges Reservoir Area, China
NaLin, Kai Ding, Libing Tan, et al.
Advances in Space Research (2025)
Closed Access
Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization
Kun Li, Junsan Zhao, Yilin Lin
Natural Hazards (2023) Vol. 118, Iss. 3, pp. 2477-2511
Open Access | Times Cited: 10
Landslide Susceptibility Assessment Considering Time-Varying of Dynamic Factors
Zhongbo Li, Chao Yin, Ziyong Tan, et al.
Natural Hazards Review (2024) Vol. 25, Iss. 3
Closed Access | Times Cited: 3
Analysis of deformation mechanism of rainfall-induced landslide in the Three Gorges Reservoir Area: Piansongshu landslide
Hui Wang, Jianhua Zou, Xinghua Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data
Jiakai Lu, Chao Ren, Weiting Yue, et al.
Sustainability (2023) Vol. 15, Iss. 18, pp. 13563-13563
Open Access | Times Cited: 7
GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
Vojislav Deđanski, Uroš Durlević, Aleksandar Kovjanić, et al.
Open Geosciences (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 2
Assessing landslide susceptibility using improved machine learning methods and considering spatial heterogeneity for the Three Gorges Reservoir Area, China
Jiahui Dong, Ruiqing Niu, Tao Chen, et al.
Natural Hazards (2023) Vol. 120, Iss. 2, pp. 1113-1140
Closed Access | Times Cited: 6
Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model
Xiaodi Fu, Bo Zhang, Linjun Wang, et al.
Frontiers in Earth Science (2023) Vol. 10
Open Access | Times Cited: 4
Evaluating landslide susceptibility: an AHP method-based approach enhanced with optimized random forest modeling
Xuedong Zhang, Haoyun Xie, Zidong Xu, et al.
Natural Hazards (2024) Vol. 120, Iss. 9, pp. 8153-8207
Closed Access | Times Cited: 1
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