Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers
Binh Thai Pham, Tran Van Phong, T. Nguyen‐Thoi, et al.
Geocarto International (2020) Vol. 37, Iss. 3, pp. 735-757
Closed Access | Times Cited: 92

Showing 1-25 of 92 citing articles:

Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Quang Hung Nguyen, Haï-Bang Ly, Lanh Si Ho, et al.
Mathematical Problems in Engineering (2021) Vol. 2021, pp. 1-15
Open Access | Times Cited: 467
Flood susceptibility modelling using advanced ensemble machine learning models
Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101075-101075
Open Access | Times Cited: 427
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
Emrehan Kutluğ Şahin
SN Applied Sciences (2020) Vol. 2, Iss. 7
Open Access | Times Cited: 258
Application of artificial intelligence in geotechnical engineering: A state-of-the-art review
Abolfazl Baghbani, Tanveer Choudhury, Susanga Costa, et al.
Earth-Science Reviews (2022) Vol. 228, pp. 103991-103991
Closed Access | Times Cited: 199
Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer
Wei Chen, Xi Chen, Jianbing Peng, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 1, pp. 93-107
Open Access | Times Cited: 167
Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
Phong Tung Nguyen, Duong Hai Ha, Mohammadtaghi Avand, et al.
Applied Sciences (2020) Vol. 10, Iss. 7, pp. 2469-2469
Open Access | Times Cited: 155
Groundwater level prediction using machine learning algorithms in a drought-prone area
Quoc Bao Pham, Manish Kumar, Fabio Di Nunno, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 13, pp. 10751-10773
Closed Access | Times Cited: 130
Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area
Chao Zhou, Ying Cao, Xie Hu, et al.
Landslides (2022) Vol. 19, Iss. 7, pp. 1585-1597
Closed Access | Times Cited: 92
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: 136
Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam
Phong Tung Nguyen, Duong Hai Ha, Abolfazl Jaafari, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 7, pp. 2473-2473
Open Access | Times Cited: 125
Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, et al.
Journal of Hydrology Regional Studies (2021) Vol. 36, pp. 100848-100848
Open Access | Times Cited: 97
Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams
Haï-Bang Ly, Tien-Thinh Le, Huong-Lan Thi Vu, et al.
Sustainability (2020) Vol. 12, Iss. 7, pp. 2709-2709
Open Access | Times Cited: 82
Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms
Javed Mallick, Saeed Alqadhi, Swapan Talukdar, et al.
Sustainability (2021) Vol. 13, Iss. 2, pp. 457-457
Open Access | Times Cited: 58
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: 40
Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
Chaitanya B. Pande, Romulus Costache, Saad Sh. Sammen, et al.
Theoretical and Applied Climatology (2023) Vol. 152, Iss. 1-2, pp. 535-558
Closed Access | Times Cited: 29
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
Novel evolutionary-optimized neural network for predicting landslide susceptibility
Rana Muhammad Adnan Ikram, Imran Khan, Hossein Moayedi, et al.
Environment Development and Sustainability (2023) Vol. 26, Iss. 7, pp. 17687-17719
Closed Access | Times Cited: 24
A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping
Hossein Moayedi, Atefeh Ahmadi Dehrashid
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 34, pp. 82964-82989
Open Access | Times Cited: 24
A novel approach for flood hazard assessment using hybridized ensemble models and feature selection algorithms
A. Habibi, M. R. Delavar, Borzoo Nazari, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 122, pp. 103443-103443
Open Access | Times Cited: 24
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: 17
Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales
Faming Huang, Yu Cao, Wenbin Li, et al.
International Journal of Coal Science & Technology (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 14
Global Review of Modification, Optimization, and Improvement Models for Aquifer Vulnerability Assessment in the Era of Climate Change
Mojgan Bordbar, Fatemeh Rezaie, Sayed M. Bateni, et al.
Current Climate Change Reports (2024) Vol. 9, Iss. 4, pp. 45-67
Closed Access | Times Cited: 9
Effective Random Forest-Based Fault Detection and Diagnosis for Wind Energy Conversion Systems
Radhia Fezai, Khaled Dhibi, Majdi Mansouri, et al.
IEEE Sensors Journal (2020) Vol. 21, Iss. 5, pp. 6914-6921
Closed Access | Times Cited: 69
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
GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility
Junpeng Huang, Sixiang Ling, Xiyong Wu, et al.
Land (2022) Vol. 11, Iss. 3, pp. 436-436
Open Access | Times Cited: 36
Page 1 - Next Page

Scroll to top