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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:

An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India
Sk Ajim Ali, Farhana Parvin, Quoc Bao Pham, et al.
Natural Hazards (2022) Vol. 113, Iss. 3, pp. 1601-1633
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

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: 28
Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan
Nafees Ali, Jian Chen, Xiaodong Fu, et al.
Remote Sensing (2024) Vol. 16, Iss. 6, pp. 988-988
Open Access | Times Cited: 14
Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
Quoc Bao Pham, Sk Ajim Ali, Farhana Parvin, et al.
Advances in Space Research (2024) Vol. 74, Iss. 1, pp. 17-47
Closed Access | Times Cited: 14
Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps
Sheela Bhuvanendran Bhagya, Anita Saji Sumi, S. Balaji, et al.
Land (2023) Vol. 12, Iss. 2, pp. 468-468
Open Access | Times Cited: 28
Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey
Ayşe Yavuz Özalp, Halil Akıncı, Mustafa Zeybek
Water (2023) Vol. 15, Iss. 14, pp. 2661-2661
Open Access | Times Cited: 22
Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India
Armin Moghimi, Chiranjit Singha, Mahdiyeh Fathi, et al.
Quaternary Science Advances (2024) Vol. 14, pp. 100187-100187
Open Access | Times Cited: 10
Implication of novel hybrid machine learning model for flood subsidence susceptibility mapping: A representative case study in Saudi Arabia
Ahmed M. Al‐Areeq, Radhwan A. A. Saleh, Mustafa Ghaleb, et al.
Journal of Hydrology (2024) Vol. 630, pp. 130692-130692
Closed Access | Times Cited: 7
A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping
Radhwan A. A. Saleh, Ahmed M. Al‐Areeq, Amran A. Al Aghbari, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 5
Machine Learning-assisted Investigation of Landslide Susceptibility for Aglar Watershed in the Lesser Himalaya Region
Dipika Keshri, Shovan Lal Chattoraj, Rakesh Kumar Pandey, et al.
Journal of the Geological Society of India (2025) Vol. 101, Iss. 3, pp. 384-396
Closed Access
Comparative study on landslide susceptibility mapping based on different ratios of training samples and testing samples by using RF and FR-RF models
Ke Xu, Zhou Zhao, Wei Chen, et al.
Natural Hazards Research (2023) Vol. 4, Iss. 1, pp. 62-74
Open Access | Times Cited: 15
Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin
Indrajit Poddar, Ranjan Roy
Quaternary Science Advances (2023) Vol. 13, pp. 100150-100150
Open Access | Times Cited: 15
Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China
Wancai He, Guoping Chen, Junsan Zhao, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2549-2549
Open Access | Times Cited: 14
Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods
Paul Esteban Bravo-López, Tomás Fernández, Chester Sellers, et al.
Land (2023) Vol. 12, Iss. 6, pp. 1135-1135
Open Access | Times Cited: 13
Examining the role of class imbalance handling strategies in predicting earthquake-induced landslide-prone regions
Quoc Bao Pham, Ömer Ekmekcioğlu, Sk Ajim Ali, et al.
Applied Soft Computing (2023) Vol. 143, pp. 110429-110429
Closed Access | Times Cited: 12
Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
Fei Teng, Yimin Mao, Yican Li, et al.
Open Geosciences (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 4
Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks
Xiangqi Lei, Hanhu Liu, Zhe Chen, et al.
International Journal of Digital Earth (2025) Vol. 18, Iss. 1
Open Access
Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model
Sliman Hitouri, Meriame Mohajane, Sk Ajim Ali, et al.
International Soil and Water Conservation Research (2023) Vol. 12, Iss. 2, pp. 279-297
Open Access | Times Cited: 11
A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction
Qiyuan Yang, Xianmin Wang, Jing Yin, et al.
Geoscience Frontiers (2023) Vol. 15, Iss. 2, pp. 101770-101770
Open Access | Times Cited: 11
Progress and landscape of disaster science: Insights from computational analyses
Maziar Yazdani, Martin Loosemore, Mohammad Mojtahedi, et al.
International Journal of Disaster Risk Reduction (2024) Vol. 108, pp. 104536-104536
Open Access | Times Cited: 2
A Systematic review of machine learning based landslide susceptibility mapping
Semachew Molla Kassa
Put i saobraćaj (2024) Vol. 70, Iss. 2, pp. 23-30
Open Access | Times Cited: 2
Correlation Between Geotechnical Indexes and Landslide Occurrence in Southwestern Cyprus Using GIS and Machine Learning
Ploutarchos Tzampoglou, Dimitrios Loukidis, Paraskevas Tsangaratos, et al.
Geotechnical and Geological Engineering (2024) Vol. 43, Iss. 1
Open Access | Times Cited: 2
Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile
Francisco Parra, Jaime González, Máx Chacón, et al.
Sustainability (2023) Vol. 15, Iss. 24, pp. 16806-16806
Open Access | Times Cited: 6
Evaluation and analysis of statistical and coupling models for highway landslide susceptibility
Huadan Fan, Yuefeng Lu, Shiwei Shao, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Mass movement susceptibility prediction and infrastructural risk assessment (IRA) using GIS-based Meta classification algorithms
Sk Ajim Ali, Meriame Mohajane, Farhana Parvin, et al.
Applied Soft Computing (2023) Vol. 145, pp. 110591-110591
Closed Access | Times Cited: 5
Spatial Analysis on the Variances of Landslide Factors Using Geographically Weighted Logistic Regression in Penang Island, Malaysia
Syaidatul Azwani Zulkafli, Nuriah Abd Majid, Ruslan Rainis
Sustainability (2023) Vol. 15, Iss. 1, pp. 852-852
Open Access | Times Cited: 4
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