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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:

Hybrid computational intelligence models for groundwater potential mapping
Binh Thai Pham, Abolfazl Jaafari, Indra Prakash, et al.
CATENA (2019) Vol. 182, pp. 104101-104101
Closed Access | Times Cited: 130

Showing 26-50 of 130 citing articles:

Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam
Tran Van Phong, Phan Trọng Trịnh, Indra Prakash, et al.
Geocarto International (2019) Vol. 36, Iss. 15, pp. 1685-1708
Closed Access | Times Cited: 92
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
Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
Haï-Bang Ly, Binh Thai Pham, Dong Van Dao, et al.
Applied Sciences (2019) Vol. 9, Iss. 18, pp. 3841-3841
Open Access | Times Cited: 90
Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm
Tran Thi Tuyen, Abolfazl Jaafari, Hoang Phan Hải Yen, et al.
Ecological Informatics (2021) Vol. 63, pp. 101292-101292
Closed Access | Times Cited: 90
Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran
Mahmoud Bayat, Mansour Ghorbanpour, Rozita Zare, et al.
Computers and Electronics in Agriculture (2019) Vol. 164, pp. 104929-104929
Closed Access | Times Cited: 89
Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India
Shruti Sachdeva, Bijendra Kumar
Stochastic Environmental Research and Risk Assessment (2020) Vol. 35, Iss. 2, pp. 287-306
Open Access | Times Cited: 81
Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration
Shahab S. Band, Saeid Janizadeh, Subodh Chandra Pal, et al.
Sensors (2020) Vol. 20, Iss. 20, pp. 5763-5763
Open Access | Times Cited: 74
Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades
Sarah Condran, Michael Bewong, Md Zahidul Islam, et al.
IEEE Access (2022) Vol. 10, pp. 73786-73803
Open Access | Times Cited: 62
Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System
Safae Ijlil, Ali Essahlaoui, Meriame Mohajane, et al.
Remote Sensing (2022) Vol. 14, Iss. 10, pp. 2379-2379
Open Access | Times Cited: 41
Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping
Fakhrul Islam, Aqil Tariq, Rufat Guluzade, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 38
Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India
Md Masroor, Haroon Sajjad, Pankaj Kumar, et al.
Water (2023) Vol. 15, Iss. 3, pp. 419-419
Open Access | Times Cited: 30
RETRACTED ARTICLE: Artificial neural networks (ANN), MARS, and adaptive network-based fuzzy inference system (ANFIS) to predict the stress at the failure of concrete with waste steel slag coarse aggregate replacement
Nzar Shakr Piro, Ahmed Salih Mohammed, Samir M. Hamad, et al.
Neural Computing and Applications (2023) Vol. 35, Iss. 18, pp. 13293-13319
Closed Access | Times Cited: 28
Advanced Machine Vision Techniques for Groundwater Level Prediction Modeling Geospatial and Statistical Research
Dai Xianglin, Aqil Tariq, Ahsan Jamil, et al.
Advances in Space Research (2024)
Closed Access | Times Cited: 10
Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction
Hoang-Long Nguyen, Binh Thai Pham, Lê Hoàng Sơn, et al.
Applied Sciences (2019) Vol. 9, Iss. 21, pp. 4715-4715
Open Access | Times Cited: 69
Multi-influencing factor method for delineation of groundwater potential zones using remote sensing and GIS techniques in the western part of Perambalur district, southern India
S. Anbarasu, K. Brindha, L. Elango
Earth Science Informatics (2019) Vol. 13, Iss. 2, pp. 317-332
Closed Access | Times Cited: 69
Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential
Romulus Costache, Quoc Bao Pham, Ema Corodescu-Roșca, et al.
Remote Sensing (2020) Vol. 12, Iss. 9, pp. 1422-1422
Open Access | Times Cited: 65
Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier
Mehebub Sahana, Binh Thai Pham, Manas Shukla, et al.
Geocarto International (2020) Vol. 37, Iss. 10, pp. 2747-2771
Closed Access | Times Cited: 56
Identification of the Groundwater Potential Recharge Zones Using MCDM Models: Full Consistency Method (FUCOM), Best Worst Method (BWM) and Analytic Hierarchy Process (AHP)
Maryam Akbari, Sarita Gajbhiye Meshram, R. S. Krishna, et al.
Water Resources Management (2021) Vol. 35, Iss. 14, pp. 4727-4745
Closed Access | Times Cited: 56
Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination
Binh Thai Pham, T. Nguyen‐Thoi, Haï-Bang Ly, et al.
Sustainability (2020) Vol. 12, Iss. 6, pp. 2339-2339
Open Access | Times Cited: 53
Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling
Phong Tung Nguyen, Duong Hai Ha, Huu Duy Nguyen, et al.
Sustainability (2020) Vol. 12, Iss. 7, pp. 2622-2622
Open Access | Times Cited: 53
Naïve Bayes ensemble models for groundwater potential mapping
Binh Thai Pham, Abolfazl Jaafari, Tran Van Phong, et al.
Ecological Informatics (2021) Vol. 64, pp. 101389-101389
Closed Access | Times Cited: 53
Proposing receiver operating characteristic-based sensitivity analysis with introducing swarm optimized ensemble learning algorithms for groundwater potentiality modelling in Asir region, Saudi Arabia
Javed Mallick, Swapan Talukdar, Majed Alsubih, et al.
Geocarto International (2021) Vol. 37, Iss. 15, pp. 4361-4389
Closed Access | Times Cited: 44
An integrated geoinformatics and hydrogeological approach to delineating groundwater potential zones in the complex geological terrain of Abuja, Nigeria
Mary Etuk, Ogbonnaya Igwe, Johnbosco C. Egbueri
Modeling Earth Systems and Environment (2022) Vol. 9, Iss. 1, pp. 285-311
Closed Access | Times Cited: 32
Global review of groundwater potential models in the last decade: Parameters, model techniques, and validation
Nguyễn Ngọc Thành, Pongsathron Thunyawatcharakul, Nguyen Huu Ngu, et al.
Journal of Hydrology (2022) Vol. 614, pp. 128501-128501
Closed Access | Times Cited: 32
Spatial Mapping of Groundwater Potentiality Applying Geometric Average and Fractal Models: A Sustainable Approach
Fatima Zahra Echogdali, Said Boutaleb, Mohamed Abioui, et al.
Water (2023) Vol. 15, Iss. 2, pp. 336-336
Open Access | Times Cited: 18

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