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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:

Study on landslide susceptibility mapping based on rock–soil characteristic factors
Xianyu Yu, Kaixiang Zhang, Yingxu Song, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis
Hoang Thi Hang, Javed Mallick, Saeed Alqadhi, et al.
Environmental Technology & Innovation (2024) Vol. 35, pp. 103655-103655
Open Access | Times Cited: 20
Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy
Mohamed M. Abdelkader, Árpád Csámer
Natural Hazards (2025)
Open Access | Times Cited: 2
Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping
Rui Liu, Xin Yang, Chong Xu, et al.
Remote Sensing (2022) Vol. 14, Iss. 2, pp. 321-321
Open Access | Times Cited: 61
Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps
Sheela Bhuvanendran Bhagya, Anita Saji Sumi, S. Balaji, et al.
Land (2023) Vol. 12, Iss. 2, pp. 468-468
Open Access | Times Cited: 28
Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution
Babitha Ganesh, Shweta Vincent, Sameena Pathan, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 29, pp. 100905-100905
Closed Access | Times Cited: 26
Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping
Ahmed M. Youssef, Bosy A. El‐Haddad, Hariklia D. Skilodimou, et al.
Natural Hazards (2024)
Closed Access | Times Cited: 15
A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau
Wenhuan Wu, Qiang Zhang, Vijay P. Singh, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4662-4662
Open Access | Times Cited: 36
Comparative study on landslide susceptibility mapping based on unbalanced sample ratio
Tang Li, Xianyu Yu, Weiwei Jiang, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 20
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: 8
Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China
Hui Shang, Lixiang Su, Wei Chen, et al.
Remote Sensing (2023) Vol. 15, Iss. 20, pp. 4952-4952
Open Access | Times Cited: 16
Landslide susceptibility assessment using novel hybridized methods based on the support vector regression
Abolfazl Jaafari
Ecological Engineering (2024) Vol. 208, pp. 107372-107372
Closed Access | Times Cited: 5
Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods
Marouane Benmakhlouf, Younès El Kharim, Jesús Galindo-Zaldı́var, et al.
Civil Engineering Journal (2023) Vol. 9, Iss. 12, pp. 3218-3232
Open Access | Times Cited: 12
Insights into spatial differential characteristics of landslide susceptibility from sub-region to whole-region cased by northeast Chongqing, China
Rui Liu, Yuekai Ding, Deliang Sun, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 11
Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network
Elham Khalili, Shahin Ramazi, Faezeh Ghanati, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Closed Access | Times Cited: 19
A Comparative Study of Shallow Machine Learning Models and Deep Learning Models for Landslide Susceptibility Assessment Based on Imbalanced Data
Shiluo Xu, Yingxu Song, Xiulan Hao
Forests (2022) Vol. 13, Iss. 11, pp. 1908-1908
Open Access | Times Cited: 19
Insights into large landslide mechanisms in tectonically active Agadir, Morocco: The significance of lithological, geomorphological, and soil characteristics
Fatima Machay, Said El Moussaoui, Hajar El Talibi
Scientific African (2023) Vol. 22, pp. e01901-e01901
Open Access | Times Cited: 11
Advanced Landslide Early Warning System Based on a Semi-supervised Model in Highly Urbanized Areas across China's Greater Bay Area
Haixia Yu, Yi Jin, Kunlong He, et al.
Research Square (Research Square) (2025)
Open Access
Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study
Jiang Li, Zhuoying Tan, Naigen Tan, et al.
Land (2025) Vol. 14, Iss. 4, pp. 678-678
Open Access
Integration of Landslide Susceptibility and Road Infrastructure Vulnerability for Risk Assessment and Mountain Road Resilience Enhancement
Keshab Sharma, Netra Prakash Bhandary, Mandip Subedi, et al.
Indian geotechnical journal (2025)
Closed Access
Utilising Machine Learning Approaches for Enhanced Landslide Susceptibility Mapping in Sikkim, India
Sujit Kumar Roy, Sumon Dey, Jayanta Das, et al.
Geological Journal (2025)
Closed Access
Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math
Xianyu Yu, Yang Xia, Jianguo Zhou, et al.
Sustainability (2023) Vol. 15, Iss. 3, pp. 2226-2226
Open Access | Times Cited: 9
A side-sampling based Linformer model for landslide susceptibility assessment: a case study of the railways in China
Nan Jiang, Yange Li, Zheng Han, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
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
A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping
Deliang Sun, Yuekai Ding, Haijia Wen, et al.
Earth Surface Processes and Landforms (2023) Vol. 49, Iss. 1, pp. 304-317
Open Access | Times Cited: 5
Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis
Saeed Alqadhi, Javed Mallick, Hoang Thi Hang, et al.
Environmental Science and Pollution Research (2023) Vol. 31, Iss. 2, pp. 3169-3194
Closed Access | Times Cited: 4
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