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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 hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China
Yumiao Wang, Luwei Feng, Sijia Li, et al.
CATENA (2020) Vol. 188, pp. 104425-104425
Closed Access | Times Cited: 161

Showing 1-25 of 161 citing articles:

Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest
Deliang Sun, Jiahui Xu, Haijia Wen, et al.
Engineering Geology (2020) Vol. 281, pp. 105972-105972
Closed Access | Times Cited: 285
Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble
Haoyuan Hong, Junzhi Liu, A‐Xing Zhu
The Science of The Total Environment (2020) Vol. 718, pp. 137231-137231
Closed Access | Times Cited: 175
Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms
Abdul‐Lateef Balogun, Fatemeh Rezaie, Quoc Bao Pham, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101104-101104
Open Access | Times Cited: 141
A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping
Liang Lv, Tao Chen, Jie Dou, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 108, pp. 102713-102713
Open Access | Times Cited: 116
A comprehensive review of machine learning‐based methods in landslide susceptibility mapping
Songlin Liu, Luqi Wang, Wengang Zhang, et al.
Geological Journal (2023) Vol. 58, Iss. 6, pp. 2283-2301
Closed Access | Times Cited: 107
Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
Moziihrii Ado, Khwairakpam Amitab, Arnab Kumar Maji, et al.
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 3029-3029
Open Access | Times Cited: 106
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
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: 80
Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China
Hanxiang Xiong, Chuanming Ma, Minghong Li, et al.
The Science of The Total Environment (2023) Vol. 866, pp. 161430-161430
Closed Access | Times Cited: 52
A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples
Deliang Sun, WU Xiao-qing, Haijia Wen, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 49
How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? — A catchment-scale case study from China
Zizheng Guo, Bixia Tian, Yuhang Zhu, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024) Vol. 16, Iss. 3, pp. 877-894
Open Access | Times Cited: 30
Advanced integration of ensemble learning and MT-InSAR for enhanced slow-moving landslide susceptibility zoning
Taorui Zeng, Liyang Wu, Yuichi S. Hayakawa, et al.
Engineering Geology (2024) Vol. 331, pp. 107436-107436
Closed Access | Times Cited: 25
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
Binh Thai Pham, T. Nguyen‐Thoi, Chongchong Qi, et al.
CATENA (2020) Vol. 195, pp. 104805-104805
Closed Access | Times Cited: 134
Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method
Sunil Saha, Alireza Arabameri, Anik Saha, et al.
The Science of The Total Environment (2020) Vol. 764, pp. 142928-142928
Closed Access | Times Cited: 105
Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping
Haoyuan Hong, Paraskevas Tsangaratos, Ioanna Ilia, et al.
The Science of The Total Environment (2020) Vol. 742, pp. 140549-140549
Closed Access | Times Cited: 95
Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments
Wei Chen, Yunzhi Chen, Paraskevas Tsangaratos, et al.
Remote Sensing (2020) Vol. 12, Iss. 23, pp. 3854-3854
Open Access | Times Cited: 83
Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India
Shruti Sachdeva, Bijendra Kumar
Stochastic Environmental Research and Risk Assessment (2020) Vol. 35, Iss. 2, pp. 287-306
Open Access | Times Cited: 81
Rapidly assessing earthquake-induced landslide susceptibility on a global scale using random forest
Qian He, Ming Wang, Kai Liu
Geomorphology (2021) Vol. 391, pp. 107889-107889
Closed Access | Times Cited: 81
Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping
Wang Shibao, Jianqi Zhuang, Jia Zheng, et al.
Frontiers in Earth Science (2021) Vol. 9
Open Access | Times Cited: 73
Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods
Qian He, Ziyu Jiang, Ming Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1572-1572
Open Access | Times Cited: 63
A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods
Pengxiang Zhao, Zohreh Masoumi, Maryam Kalantari, et al.
Remote Sensing (2022) Vol. 14, Iss. 1, pp. 211-211
Open Access | Times Cited: 54
Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods
Zhilong Xi, Huadong Xu, Yanqiu Xing, et al.
Remote Sensing (2022) Vol. 14, Iss. 2, pp. 364-364
Open Access | Times Cited: 42
Effectiveness of Newmark-based sampling strategy for coseismic landslide susceptibility mapping using deep learning, support vector machine, and logistic regression
Chuanjie Xi, Mei Han, Xiewen Hu, et al.
Bulletin of Engineering Geology and the Environment (2022) Vol. 81, Iss. 5
Closed Access | Times Cited: 40
Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System
Safae Ijlil, Ali Essahlaoui, Meriame Mohajane, et al.
Remote Sensing (2022) Vol. 14, Iss. 10, pp. 2379-2379
Open Access | Times Cited: 38
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: 35
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