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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 at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods
Abdelaziz Merghadi, Abderrahmane Boumezbeur, Dieu Tien Bui
ISPRS International Journal of Geo-Information (2018) Vol. 7, Iss. 7, pp. 268-268
Open Access | Times Cited: 154

Showing 26-50 of 154 citing articles:

Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China
Xudong Hu, Han Zhang, Hongbo Mei, et al.
Applied Sciences (2020) Vol. 10, Iss. 11, pp. 4016-4016
Open Access | Times Cited: 70
Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks
Luísa Vieira Lucchese, Guilherme Garcia de Oliveira, Olavo Corrêa Pedrollo
CATENA (2020) Vol. 198, pp. 105067-105067
Closed Access | Times Cited: 70
Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh
Bayes Ahmed, Md. Shahinoor Rahman, Rahenul Islam, et al.
ISPRS International Journal of Geo-Information (2018) Vol. 7, Iss. 12, pp. 485-485
Open Access | Times Cited: 68
Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques
Harjeet Kaur, Srimanta Gupta, Surya Parkash, et al.
Annals of GIS (2019) Vol. 25, Iss. 2, pp. 113-132
Open Access | Times Cited: 58
Unraveling the drivers of intensified landslide regimes in Western Ghats, India
Ali P. Yunus, Xuanmei Fan, Srikrishnan Siva Subramanian, et al.
The Science of The Total Environment (2021) Vol. 770, pp. 145357-145357
Closed Access | Times Cited: 50
Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace–based naïve Bayes tree in Zigui County of the Three Gorges Reservoir Area, China
Xudong Hu, Cheng Huang, Hongbo Mei, et al.
Bulletin of Engineering Geology and the Environment (2021) Vol. 80, Iss. 7, pp. 5315-5329
Closed Access | Times Cited: 43
Application of a two-step sampling strategy based on deep neural network for landslide susceptibility mapping
Jingyu Yao, Shengwu Qin, Shuangshuang Qiao, et al.
Bulletin of Engineering Geology and the Environment (2022) Vol. 81, Iss. 4
Closed Access | Times Cited: 35
Development of an efficient cement production monitoring system based on the improved random forest algorithm
Hanane Zermane, Abbes Drardja
The International Journal of Advanced Manufacturing Technology (2022) Vol. 120, Iss. 3-4, pp. 1853-1866
Open Access | Times Cited: 32
Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost
Emrehan Kutluğ Şahin
Stochastic Environmental Research and Risk Assessment (2022) Vol. 37, Iss. 3, pp. 1067-1092
Closed Access | Times Cited: 30
Community perceptions of landslide risk and susceptibility: a multi-country study
Moeen Hamid Bukhari, Paula F. da Silva, Jürgen Pilz, et al.
Landslides (2023) Vol. 20, Iss. 6, pp. 1321-1334
Closed Access | Times Cited: 21
Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms
Abdul Baser Qasimi, Vahid Isazade, Enayatullah Enayat, et al.
Geocarto International (2023) Vol. 38, Iss. 1
Open Access | Times Cited: 17
Geological Disaster Susceptibility Evaluation Using a Random Forest Empowerment Information Quantity Model
Rongwei Li, Shucheng Tan, Mingfei Zhang, et al.
Sustainability (2024) Vol. 16, Iss. 2, pp. 765-765
Open Access | Times Cited: 7
Ensemble learning landslide susceptibility assessment with optimized non-landslide samples selection
Jiangang Lu, Yi He, Lifeng Zhang, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 7
Landslide susceptibility mapping in three Upazilas of Rangamati hill district Bangladesh: application and comparison of GIS-based machine learning methods
Yasin Wahid Rabby, Md Belal Hossain, Joynal Abedin
Geocarto International (2020) Vol. 37, Iss. 12, pp. 3371-3396
Closed Access | Times Cited: 50
Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning
Jhe-Syuan Lai, Fuan Tsai
Sensors (2019) Vol. 19, Iss. 17, pp. 3717-3717
Open Access | Times Cited: 49
Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China
Weidong Wang, Zhuolei He, Zheng Han, et al.
Natural Hazards (2020) Vol. 103, Iss. 3, pp. 3239-3261
Closed Access | Times Cited: 46
Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China
Jingyu Yao, Shengwu Qin, Shuangshuang Qiao, et al.
Applied Sciences (2020) Vol. 10, Iss. 16, pp. 5640-5640
Open Access | Times Cited: 44
Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping
Taşkın Kavzoğlu, Alihan Teke, Elif Özlem Yılmaz
Remote Sensing (2021) Vol. 13, Iss. 23, pp. 4776-4776
Open Access | Times Cited: 38
A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping
Zhu Liang, Changming Wang, Zhijie Duan, et al.
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1464-1464
Open Access | Times Cited: 36
Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria)
Amina Abdı, Ali Bouamrane, Toufik Karech, et al.
Geotechnical and Geological Engineering (2021) Vol. 39, Iss. 8, pp. 5675-5691
Closed Access | Times Cited: 34
Basin-wide flood depth and exposure mapping from SAR images and machine learning models
Hao Chen, Ali P. Yunus, Srikrishnan Siva Subramanian, et al.
Journal of Environmental Management (2021) Vol. 297, pp. 113367-113367
Closed Access | Times Cited: 33
Post-earthquake damage classification and assessment: case study of the residential buildings after the Mw = 5 earthquake in Mila city, Northeast Algeria on August 7, 2020
Mouloud Hamidatou, Amar Chaker, Nassim Hallal, et al.
Bulletin of Earthquake Engineering (2022) Vol. 21, Iss. 2, pp. 849-891
Open Access | Times Cited: 24
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
Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods
Esteban Bravo-López, Tomás Fernández, Chester Sellers, et al.
Land (2023) Vol. 12, Iss. 6, pp. 1135-1135
Open Access | Times Cited: 15
A comparative study of heterogeneous and homogeneous ensemble approaches for landslide susceptibility assessment in the Djebahia region, Algeria
Zakaria Matougui, Lynda Djerbal, Ramdane Bahar
Environmental Science and Pollution Research (2023) Vol. 31, Iss. 28, pp. 40554-40580
Closed Access | Times Cited: 14

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