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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 novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment
Binh Thai Pham, Indra Prakash
Bulletin of Engineering Geology and the Environment (2017) Vol. 78, Iss. 3, pp. 1911-1925
Closed Access | Times Cited: 72

Showing 26-50 of 72 citing articles:

Prediction and evaluation of energy and exergy efficiencies of a nanofluid-based photovoltaic-thermal system with a needle finned serpentine channel using random forest machine learning approach
Yuanlei Si, František Brumerčík, Yang Chun-sheng, et al.
Engineering Analysis with Boundary Elements (2023) Vol. 151, pp. 328-343
Closed Access | Times Cited: 25
Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling
Qingfeng He, Zhihao Xu, Shaojun Li, et al.
Entropy (2019) Vol. 21, Iss. 2, pp. 106-106
Open Access | Times Cited: 65
Bagging-based machine learning algorithms for landslide susceptibility modeling
Tingyu Zhang, Quan Fu, Hao Wang, et al.
Natural Hazards (2021) Vol. 110, Iss. 2, pp. 823-846
Open Access | Times Cited: 50
A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping
Xin Wei, Lulu Zhang, Junyao Luo, et al.
Natural Hazards (2021) Vol. 109, Iss. 1, pp. 471-497
Closed Access | Times Cited: 49
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
A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil
César Falcão Barella, Frederico Garcia Sobreira, José Luı́s Zêzere
Bulletin of Engineering Geology and the Environment (2018) Vol. 78, Iss. 5, pp. 3205-3221
Closed Access | Times Cited: 53
Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China
Xudong Hu, Hongbo Mei, Han Zhang, et al.
Natural Hazards (2020) Vol. 105, Iss. 2, pp. 1663-1689
Closed Access | Times Cited: 50
GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam
Viet-Tien Nguyen, Trong Hien Tran, Ngoc Ha, et al.
Sustainability (2019) Vol. 11, Iss. 24, pp. 7118-7118
Open Access | Times Cited: 48
Ensemble machine learning models based on Reduced Error Pruning Tree for prediction of rainfall-induced landslides
Binh Thai Pham, Abolfazl Jaafari, T. Nguyen‐Thoi, et al.
International Journal of Digital Earth (2020) Vol. 14, Iss. 5, pp. 575-596
Closed Access | Times Cited: 48
A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping
Binh Thai Pham, Haï-Bang Ly, Wei Chen, et al.
Sustainability (2019) Vol. 11, Iss. 22, pp. 6323-6323
Open Access | Times Cited: 47
GIS-based ensemble soft computing models for landslide susceptibility mapping
Binh Thai Pham, Tran Van Phong, T. Nguyen‐Thoi, et al.
Advances in Space Research (2020) Vol. 66, Iss. 6, pp. 1303-1320
Closed Access | Times Cited: 43
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
Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region
Aastha Sharma, Haroon Sajjad, Md Hibjur Rahaman, et al.
Journal of Mountain Science (2024) Vol. 21, Iss. 7, pp. 2368-2393
Closed Access | Times Cited: 5
Assessment of Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with Kernel Logistic Regression Model
Tingyu Zhang, Ling Han, Jichang Han, et al.
Entropy (2019) Vol. 21, Iss. 2, pp. 218-218
Open Access | Times Cited: 41
Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping
Zhu Liang, Changming Wang, Kaleem Ullah Jan Khan
Stochastic Environmental Research and Risk Assessment (2020) Vol. 35, Iss. 6, pp. 1243-1256
Closed Access | Times Cited: 37
Landslides triggered by the 6 September 2018 Mw 6.6 Hokkaido, Japan: an updated inventory and retrospective hazard assessment
Yulong Cui, Pengpeng Bao, Chong Xu, et al.
Earth Science Informatics (2020) Vol. 14, Iss. 1, pp. 247-258
Closed Access | Times Cited: 33
Landslide spatial probability prediction: a comparative assessment of naïve Bayes, ensemble learning, and deep learning approaches
Ba-Quang-Vinh Nguyen, Yun-Tae Kim
Bulletin of Engineering Geology and the Environment (2021) Vol. 80, Iss. 6, pp. 4291-4321
Closed Access | Times Cited: 31
Landslide susceptibility assessment using statistical and machine learning techniques: A case study in the upper reaches of the Minjiang River, southwestern China
Sixiang Ling, Siyuan Zhao, Junpeng Huang, et al.
Frontiers in Earth Science (2022) Vol. 10
Open Access | Times Cited: 21
Hybrid Machine Learning Models for Soil Saturated Conductivity Prediction
Francesco Granata, Fabio Di Nunno, Giuseppe Modoni
Water (2022) Vol. 14, Iss. 11, pp. 1729-1729
Open Access | Times Cited: 20
Random Forest and Logistic Regression algorithms for prediction of groundwater contamination using ammonia concentration
Ahmed Madani, Mohammed Hagage, Salwa F. Elbeih
Arabian Journal of Geosciences (2022) Vol. 15, Iss. 20
Open Access | Times Cited: 20
Application of ensemble learning in predicting shallow foundation settlement in cohesionless soil
Ningthoujam Jibanchand, Konsam Rambha Devi
International Journal of Geotechnical Engineering (2023) Vol. 17, Iss. 3, pp. 234-245
Closed Access | Times Cited: 11
Novel framework for modelling the cadmium balance and accumulation in farmland soil in Zhejiang Province, East China: Sensitivity analysis, parameter optimisation, and forecast for 2050
Tingting Fu, Ruiying Zhao, Bifeng Hu, et al.
Journal of Cleaner Production (2020) Vol. 279, pp. 123674-123674
Open Access | Times Cited: 30
Predicting susceptibility to landslides under climate change impacts in metropolitan areas of South Korea using machine learning
Sang-Jin Park, Dong-kun Lee
Geomatics Natural Hazards and Risk (2021) Vol. 12, Iss. 1, pp. 2462-2476
Open Access | Times Cited: 25
Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping
Saeed Alqadhi, Javed Mallick, Swapan Talukdar, et al.
Geocarto International (2021) Vol. 37, Iss. 25, pp. 9518-9543
Closed Access | Times Cited: 25
Susceptibility mapping of damming landslide based on slope unit using frequency ratio model
Hanhu Liu, Xingong Li, Tian Meng, et al.
Arabian Journal of Geosciences (2020) Vol. 13, Iss. 16
Closed Access | Times Cited: 24

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