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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 in complex geological settings: sensitivity to geological information and insights on its parameterization
Samuele Segoni, Giulio Pappafico, Tania Luti, et al.
Landslides (2020) Vol. 17, Iss. 10, pp. 2443-2453
Open Access | Times Cited: 115

Showing 1-25 of 115 citing articles:

Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model
Junyi Zhang, Xianglong Ma, Jialan Zhang, et al.
Journal of Environmental Management (2023) Vol. 332, pp. 117357-117357
Closed Access | Times Cited: 223
Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability
Mariano Di Napoli, Francesco Carotenuto, Andrea Cevasco, et al.
Landslides (2020) Vol. 17, Iss. 8, pp. 1897-1914
Closed Access | Times Cited: 205
GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms
Sk Ajim Ali, Farhana Parvin, Jana Vojteková, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 857-876
Open Access | Times Cited: 197
Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management
Zizheng Guo, Yu Shi, Faming Huang, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 6, pp. 101249-101249
Open Access | Times Cited: 175
Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale
Vicente Medina, Marcel Hürlimann, Zizheng Guo, et al.
CATENA (2021) Vol. 201, pp. 105213-105213
Open Access | Times Cited: 143
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: 89
Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China
Zizheng Guo, Joaquin V. Ferrer, Marcel Hürlimann, et al.
Geoscience Frontiers (2023) Vol. 14, Iss. 4, pp. 101542-101542
Open Access | Times Cited: 68
Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China
Lanbing Yu, Yang Wang, Biswajeet Pradhan
Geoscience Frontiers (2024) Vol. 15, Iss. 4, pp. 101802-101802
Open Access | Times Cited: 21
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: 21
Rapidly Evolving Controls of Landslides After a Strong Earthquake and Implications for Hazard Assessments
Xuanmei Fan, Ali P. Yunus, Gianvito Scaringi, et al.
Geophysical Research Letters (2020) Vol. 48, Iss. 1
Open Access | Times Cited: 94
Impacts of future climate and land cover changes on landslide susceptibility: regional scale modelling in the Val d’Aran region (Pyrenees, Spain)
Marcel Hürlimann, Zizheng Guo, Càrol Puig i Polo, et al.
Landslides (2021) Vol. 19, Iss. 1, pp. 99-118
Closed Access | Times Cited: 89
Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions
Deuk-Hwan Lee, Yun-Tae Kim, Seung-Rae Lee
Remote Sensing (2020) Vol. 12, Iss. 7, pp. 1194-1194
Open Access | Times Cited: 76
Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides
Abolfazl Jaafari, Mahdi Panahi, Davood Mafi-Gholami, et al.
Applied Soft Computing (2021) Vol. 116, pp. 108254-108254
Closed Access | Times Cited: 74
Shallow landslides and vegetation at the catchment scale: A perspective
Chris Phillips, Tristram C. Hales, Hugh G. Smith, et al.
Ecological Engineering (2021) Vol. 173, pp. 106436-106436
Open Access | Times Cited: 58
Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)
Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, et al.
Natural hazards and earth system sciences (2022) Vol. 22, Iss. 4, pp. 1395-1417
Open Access | Times Cited: 46
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: 45
Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method
Faming Huang, Zuokui Teng, Chi Yao, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 16, Iss. 1, pp. 213-230
Open Access | Times Cited: 30
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh
Md. Sharafat Chowdhury, Md. Naimur Rahman, Md. Sujon Sheikh, et al.
Heliyon (2023) Vol. 10, Iss. 1, pp. e23424-e23424
Open Access | Times Cited: 29
Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques-a case study from middle Yellow River catchment
Zizheng Guo, Bixia Tian, Guangming Li, et al.
Frontiers in Earth Science (2023) Vol. 10
Open Access | Times Cited: 27
Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting
Nicola Nocentini, Ascanio Rosi, Samuele Segoni, et al.
Frontiers in Earth Science (2023) Vol. 11
Open Access | Times Cited: 24
Application and interpretability of ensemble learning for landslide susceptibility mapping along the Three Gorges Reservoir area, China
Bo Liu, Haixiang Guo, Jinling Li, et al.
Natural Hazards (2024) Vol. 120, Iss. 5, pp. 4601-4632
Closed Access | Times Cited: 14
Impact of Non-Landslide Sample Sampling Strategies and Model Selection on Landslide Susceptibility Mapping
Weijun Jiang, Ling Li, Ruiqing Niu
Applied Sciences (2025) Vol. 15, Iss. 4, pp. 2132-2132
Open Access | Times Cited: 1
Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM
Chenglong Yu, Jianping Chen
Symmetry (2020) Vol. 12, Iss. 6, pp. 1047-1047
Open Access | Times Cited: 62
Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data
Emmanouil Psomiadis, Andreas Papazachariou, Konstantinos X. Soulis, et al.
Land (2020) Vol. 9, Iss. 5, pp. 133-133
Open Access | Times Cited: 60
Different Approaches to Use Morphometric Attributes in Landslide Susceptibility Mapping Based on Meso-Scale Spatial Units: A Case Study in Rio de Janeiro (Brazil)
Vanessa Canavesi, Samuele Segoni, Ascanio Rosi, et al.
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1826-1826
Open Access | Times Cited: 58
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