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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:

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors
Xiangang Luo, Feikai Lin, Shuang Zhu, et al.
PLoS ONE (2019) Vol. 14, Iss. 4, pp. e0215134-e0215134
Open Access | Times Cited: 87

Showing 1-25 of 87 citing articles:

Effects of non-landslide sampling strategies on machine learning models in landslide susceptibility mapping
Tengfei Gu, Ping Duan, Mingguo Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 23
An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest
Xin Xiao, Yi Zou, Jiangcheng Huang, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 18
Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India
Mehebub Sahana, Sufia Rehman, Haroon Sajjad, et al.
CATENA (2020) Vol. 189, pp. 104450-104450
Closed Access | Times Cited: 134
Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran
Peyman Yariyan, Hasan Zabihi, Isabelle D. Wolf, et al.
International Journal of Disaster Risk Reduction (2020) Vol. 50, pp. 101705-101705
Closed Access | Times Cited: 123
Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China
Wei Xie, Wen Nie, Pooya Saffari, et al.
Natural Hazards (2021) Vol. 109, Iss. 1, pp. 931-948
Closed Access | Times Cited: 103
Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation
Dahee Jung, Yosoon Choi
Minerals (2021) Vol. 11, Iss. 2, pp. 148-148
Open Access | Times Cited: 87
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: 86
Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack
Emrehan Kutluğ Şahin, İsmail Çölkesen, Suheda Semih Acmali, et al.
Computers & Geosciences (2020) Vol. 144, pp. 104592-104592
Closed Access | Times Cited: 74
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
A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
Bahareh Ghasemian, Himan Shahabi, Ataollah Shirzadi, et al.
Sensors (2022) Vol. 22, Iss. 4, pp. 1573-1573
Open Access | Times Cited: 44
Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
Sharad Kumar Gupta, Dericks Praise Shukla
Landslides (2022) Vol. 20, Iss. 5, pp. 933-949
Closed Access | Times Cited: 44
Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River
Jiaming Yao, Xin Yao, Zheng Zhao, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 26
Landslide Risk Evaluation in Shenzhen Based on Stacking Ensemble Learning and InSAR
Binghai Gao, Yi He, Xueye Chen, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 1-18
Open Access | Times Cited: 25
Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests
Maher Ibrahim Sameen, Raju Sarkar, Biswajeet Pradhan, et al.
Computers & Geosciences (2019) Vol. 134, pp. 104336-104336
Open Access | Times Cited: 56
Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong
Yangyang Chen, Dongping Ming, Ling Xiao, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021) Vol. 14, pp. 3625-3639
Open Access | Times Cited: 49
Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey
Mahyat Shafapour Tehrany, Peyman Yariyan, Haluk Özener, et al.
International Journal of Disaster Risk Reduction (2022) Vol. 79, pp. 103154-103154
Closed Access | Times Cited: 35
A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence
Haoran Fang, Yun Shao, Chou Xie, et al.
Sustainability (2023) Vol. 15, Iss. 4, pp. 3094-3094
Open Access | Times Cited: 23
Deep learning implementations in mining applications: a compact critical review
Faris Azhari, Charlotte Sennersten, Craig A. Lindley, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. 12, pp. 14367-14402
Open Access | Times Cited: 21
An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan
Kounghoon Nam, Fawu Wang
Geoenvironmental Disasters (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 44
A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
Biswajeet Pradhan, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4521-4521
Open Access | Times Cited: 36
An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries
Wenfeng Chang, Xiao Wang, Jing Yang, et al.
Sensors (2023) Vol. 23, Iss. 4, pp. 1811-1811
Open Access | Times Cited: 14
Rule-based fuzzy inference system for landslide susceptibility mapping along national highway 7 in Garhwal Himalayas, India
Shubham Badola, Varun Narayan Mishra, Surya Parkash, et al.
Quaternary Science Advances (2023) Vol. 11, pp. 100093-100093
Open Access | Times Cited: 14
Landslide displacement prediction based on time series and long short-term memory networks
Anjie Jin, Shasha Yang, Huang Xu-Ri
Bulletin of Engineering Geology and the Environment (2024) Vol. 83, Iss. 7
Closed Access | Times Cited: 6
A Deep Neural Network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall
Binghai Gao, Yi He, Xueye Chen, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 5946-5969
Open Access | Times Cited: 5
Landslide susceptibility assessment using deep learning considering unbalanced samples distribution
Deborah Simon Mwakapesa, Xiaoji Lan, Yimin Mao
Heliyon (2024) Vol. 10, Iss. 9, pp. e30107-e30107
Open Access | Times Cited: 5
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