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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 comparison of slope units and grid cells as mapping units for landslide susceptibility assessment
Qianqian Ba, Yumin Chen, Susu Deng, et al.
Earth Science Informatics (2018) Vol. 11, Iss. 3, pp. 373-388
Closed Access | Times Cited: 108

Showing 1-25 of 108 citing articles:

Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling
Wei Chen, Shuai Zhang, Renwei Li, et al.
The Science of The Total Environment (2018) Vol. 644, pp. 1006-1018
Closed Access | Times Cited: 412
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: 249
Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors
Zhilu Chang, Filippo Catani, Faming Huang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 15, Iss. 5, pp. 1127-1143
Open Access | Times Cited: 127
Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory
Marco Loche, Massimiliano Alvioli, Ivan Marchesini, et al.
Earth-Science Reviews (2022) Vol. 232, pp. 104125-104125
Open Access | Times Cited: 87
Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing
Wengang Zhang, Yuwei He, Luqi Wang, et al.
Geological Journal (2023) Vol. 58, Iss. 6, pp. 2372-2387
Closed Access | Times Cited: 84
GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India
Jayanta Das, Pritam Saha, Rajib Mitra, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16186-e16186
Open Access | Times Cited: 48
Landslide susceptibility assessment using information quantity and machine learning integrated models: a case study of Sichuan province, southwestern China
Pengtao Zhao, Ying Wang, Yi Xie, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 2
Closed Access | Times Cited: 3
Parameter-free delineation of slope units and terrain subdivision of Italy
Massimiliano Alvioli, Fausto Guzzetti, Ivan Marchesini
Geomorphology (2020) Vol. 358, pp. 107124-107124
Open Access | Times Cited: 129
Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments
Faming Huang, Siyu Tao, Zhilu Chang, et al.
Landslides (2021) Vol. 18, Iss. 11, pp. 3715-3731
Closed Access | Times Cited: 100
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
Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models
Zhou Zhao, Zeng yuan Liu, Chong Xu
Frontiers in Earth Science (2021) Vol. 9
Open Access | Times Cited: 67
Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas
Hui Deng, Xiantan Wu, Wenjiang Zhang, et al.
Remote Sensing (2022) Vol. 14, Iss. 17, pp. 4245-4245
Open Access | Times Cited: 51
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
Refined landslide susceptibility analysis based on InSAR technology and UAV multi-source data
Chen Cao, Kuanxing Zhu, Peihua Xu, et al.
Journal of Cleaner Production (2022) Vol. 368, pp. 133146-133146
Closed Access | Times Cited: 46
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: 42
A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines
Haijia Wen, Lei Liu, Jialan Zhang, et al.
Journal of Environmental Management (2023) Vol. 342, pp. 118177-118177
Closed Access | Times Cited: 37
Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China
Song Yingze, Song Yingxu, Xin Zhang, et al.
Natural Hazards (2024) Vol. 120, Iss. 8, pp. 7627-7652
Closed Access | Times Cited: 10
A SHAP-Enhanced XGBoost Model for Interpretable Prediction of Coseismic Landslides
Haijia Wen, Bo Liu, Mingrui Di, et al.
Advances in Space Research (2024) Vol. 74, Iss. 8, pp. 3826-3854
Closed Access | Times Cited: 9
Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas
Muhammad Afaq Hussain, Zhanlong Chen, Yulong Zhou, et al.
Landslides (2025)
Closed Access | Times Cited: 1
The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China
Zhuo Chen, Fei Ye, Wenxi Fu, et al.
Natural Hazards (2020) Vol. 101, Iss. 3, pp. 853-877
Closed Access | Times Cited: 68
Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China
Wei Chen, Limin Fan, Cheng Li, et al.
Applied Sciences (2019) Vol. 10, Iss. 1, pp. 29-29
Open Access | Times Cited: 66
Spatial Proximity-Based Geographically Weighted Regression Model for Landslide Susceptibility Assessment: A Case Study of Qingchuan Area, China
Yange Li, Xintong Liu, Zheng Han, et al.
Applied Sciences (2020) Vol. 10, Iss. 3, pp. 1107-1107
Open Access | Times Cited: 56
A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units
Shuai Zhao, Zhou Zhao
Mathematical Problems in Engineering (2021) Vol. 2021, pp. 1-15
Open Access | Times Cited: 51
Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador
Esteban Bravo-López, Tomás Fernández, Chester Sellers, et al.
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3495-3495
Open Access | Times Cited: 30
GIS-based evolution and comparisons of landslide susceptibility mapping of the East Sikkim Himalaya
Neha Gupta, Sanjit Kumar Pal, Josodhir Das
Annals of GIS (2022) Vol. 28, Iss. 3, pp. 359-384
Open Access | Times Cited: 29
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