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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:

The influences of the spatial extent selection for non-landslide samples on statistical-based landslide susceptibility modelling: a case study of Anhui Province in China
Chuhan Wang, Qigen Lin, Leibin Wang, et al.
Natural Hazards (2022) Vol. 112, Iss. 3, pp. 1967-1988
Closed Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

Uncertainties of landslide susceptibility prediction considering different landslide types
Faming Huang, Haowen Xiong, Chi Yao, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 15, Iss. 11, pp. 2954-2972
Open Access | Times Cited: 70
Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory
Faming Huang, Haowen Xiong, Shui‐Hua Jiang, et al.
Earth-Science Reviews (2024) Vol. 250, pp. 104700-104700
Closed Access | Times Cited: 61
Regional early warning model for rainfall induced landslide based on slope unit in Chongqing, China
Shuhao Liu, Juan Du, Kunlong Yin, et al.
Engineering Geology (2024) Vol. 333, pp. 107464-107464
Closed Access | Times Cited: 23
Improving pixel-based regional landslide susceptibility mapping
Xin Wei, Paolo Gardoni, Lulu Zhang, et al.
Geoscience Frontiers (2024) Vol. 15, Iss. 4, pp. 101782-101782
Open Access | Times Cited: 21
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: 23
Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment
Yueyue Wang, Xueling Wu, Kun Zou, et al.
Geo-spatial Information Science (2025), pp. 1-21
Open Access | Times Cited: 1
Influences of non-landslide sample selection strategies on landslide susceptibility mapping by machine learning
Hongqiang Dou, Junbin He, Siyi Huang, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 21
A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy
Jiaxin Xu, Qiaomei Su, Xiaotao Li, et al.
Remote Sensing (2024) Vol. 16, Iss. 1, pp. 200-200
Open Access | Times Cited: 8
Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique
Chao Zhou, Yue Wang, Ying Cao, et al.
Geocarto International (2024) Vol. 39, Iss. 1
Open Access | Times Cited: 7
Projected land use changes in the Qinghai-Tibet Plateau at the carbon peak and carbon neutrality targets
Runhong Xu, Peijun Shi, Miaoni Gao, et al.
Science China Earth Sciences (2023) Vol. 66, Iss. 6, pp. 1383-1398
Closed Access | Times Cited: 14
Using statistical functions and hydro-hydraulic models to develop human vulnerability curves for flash floods: The flash flood of the Taitou catchment (China) in 2016
Tianxue Liu, Ying Wang, Haiyang Yu, et al.
International Journal of Disaster Risk Reduction (2022) Vol. 73, pp. 102876-102876
Closed Access | Times Cited: 21
Geoinformatics-based frequency ratio, analytic hierarchy process and hybrid models for landslide susceptibility zonation in Kurdistan Region, Northern Iraq
Kaiwan K. Fatah, Yaseen T. Mustafa, Imaddadin O. Hassan
Environment Development and Sustainability (2023) Vol. 26, Iss. 3, pp. 6977-7014
Closed Access | Times Cited: 13
Landslide Susceptibility Mapping Based on Resampling Method and FR-CNN: A Case Study of Changdu
Zili Qin, Xinyao Zhou, Mengyao Li, et al.
Land (2023) Vol. 12, Iss. 6, pp. 1213-1213
Open Access | Times Cited: 13
Quantifying uncertainty in landslide susceptibility mapping due to sampling randomness
Leilei Liu, Song Zhao, Can Yang, et al.
International Journal of Disaster Risk Reduction (2024) Vol. 114, pp. 104966-104966
Closed Access | Times Cited: 4
Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China
Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, et al.
Geofluids (2022) Vol. 2022, pp. 1-32
Open Access | Times Cited: 17
Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China
Bingwei Wang, Qigen Lin, Tong Jiang, et al.
Geocarto International (2022) Vol. 38, Iss. 1
Open Access | Times Cited: 17
The influence of the selection of non-geological disasters sample spatial range on the evaluation of environmental geological disasters susceptibility: a case study of Liulin County
Jianping Chen, Zepeng Wang, Wei Chen, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 15, pp. 44756-44772
Closed Access | Times Cited: 10
An improved information quantity method for non-landslide selection to enhance landslide susceptibility evaluation: a case study in Yongfeng, South China
Shuhua Zhai, Yue Sun, Jiantao Lei, et al.
Natural Hazards (2025)
Closed Access
A novel landslide susceptibility mapping method based on a backpropagation neural network algorithm with optimized non-landslide samples and hyperparameters
Sheng Liu, Xin'ang Jiang, Xudong He, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 155, pp. 111052-111052
Closed Access
Landslide susceptibility mapping using an entropy index-based negative sample selection strategy: A case study of Luolong county
Yuzhong Kong, Wu Hua, Chong Xu, et al.
PLoS ONE (2025) Vol. 20, Iss. 5, pp. e0322566-e0322566
Open Access
Optimizing the Application of Machine Learning Models in Predicting Landslide Susceptibility Using the Information Value Model in Junlian County of Sichuan Basin
Lijun Qian, Lihua Ou, Guoxin Li, et al.
Advances in Space Research (2025)
Closed Access
Landslide susceptibility prediction modelling based on semi‐supervised XGBoost model
Qiangqiang Shua, Hongbin Peng, Jingkai Li
Geological Journal (2024) Vol. 59, Iss. 9, pp. 2655-2667
Closed Access | Times Cited: 3
A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning
Ann-Kathrin Edrich, Anil Yıldiz, Ribana Roscher, et al.
Natural Hazards (2024) Vol. 120, Iss. 9, pp. 8953-8982
Open Access | Times Cited: 2
Multi-scale analysis of the susceptibility of different landslide types and identification of the main controlling factors
Yuqian Yang, Shuangyun Peng, Bangmei Huang, et al.
Ecological Indicators (2024) Vol. 168, pp. 112797-112797
Open Access | Times Cited: 2
China’s Socioeconomic and CO2 Status Concerning Future Land-Use Change under the Shared Socioeconomic Pathways
Jian Zhou, Shan Jiang, Sanjit Kumar Mondal, et al.
Sustainability (2022) Vol. 14, Iss. 5, pp. 3065-3065
Open Access | Times Cited: 11
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