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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:

Groundwater potential mapping using a novel data-mining ensemble model
Mojtaba Kordestani, Seyed Amir Naghibi, Hossein Hashemi, et al.
Hydrogeology Journal (2018) Vol. 27, Iss. 1, pp. 211-224
Open Access | Times Cited: 153

Showing 1-25 of 153 citing articles:

Groundwater level prediction using machine learning models: A comprehensive review
Tao Hai, Mohammed Majeed Hameed, Haydar Abdulameer Marhoon, et al.
Neurocomputing (2022) Vol. 489, pp. 271-308
Open Access | Times Cited: 265
Machine learning in geo- and environmental sciences: From small to large scale
Pejman Tahmasebi, Serveh Kamrava, Tao Bai, et al.
Advances in Water Resources (2020) Vol. 142, pp. 103619-103619
Closed Access | Times Cited: 222
Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
Amirhosein Mosavi, Farzaneh Sajedi Hosseini, Bahram Choubin, et al.
Water Resources Management (2020) Vol. 35, Iss. 1, pp. 23-37
Closed Access | Times Cited: 215
GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches
Alireza Arabameri, Khalil Rezaei, Artemi Cerdà, et al.
The Science of The Total Environment (2018) Vol. 658, pp. 160-177
Open Access | Times Cited: 195
Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia
Omid Rahmati, Fatemeh Falah, Kavina Dayal, et al.
The Science of The Total Environment (2019) Vol. 699, pp. 134230-134230
Closed Access | Times Cited: 174
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
Machine learning for hydrologic sciences: An introductory overview
Tianfang Xu, Feng Liang
Wiley Interdisciplinary Reviews Water (2021) Vol. 8, Iss. 5
Closed Access | Times Cited: 152
Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India
Kanak N. Moharir, Chaitanya B. Pande, Vinay Kumar Gautam, et al.
Environmental Research (2023) Vol. 228, pp. 115832-115832
Closed Access | Times Cited: 125
Novel ensemble machine learning models in flood susceptibility mapping
Pankaj Prasad, Victor J. Loveson, Bappa Das, et al.
Geocarto International (2021) Vol. 37, Iss. 16, pp. 4571-4593
Closed Access | Times Cited: 106
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
Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data
Bahareh Kalantar, Naonori Ueda, Vahideh Saeidi, et al.
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1737-1737
Open Access | Times Cited: 130
Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis
Wei Chen, Biswajeet Pradhan, Shaojun Li, et al.
Natural Resources Research (2019) Vol. 28, Iss. 4, pp. 1239-1258
Closed Access | Times Cited: 128
Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques
Sunmin Lee, Yunjung Hyun, Saro Lee, et al.
Remote Sensing (2020) Vol. 12, Iss. 7, pp. 1200-1200
Open Access | Times Cited: 128
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
Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods
Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, et al.
The Science of The Total Environment (2019) Vol. 684, pp. 31-49
Closed Access | Times Cited: 122
The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers
Davoud Davoudi Moghaddam, Omid Rahmati, Mahdi Panahi, et al.
CATENA (2019) Vol. 187, pp. 104421-104421
Open Access | Times Cited: 119
Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
Wei Chen, Xia Zhao, Paraskevas Tsangaratos, et al.
Journal of Hydrology (2020) Vol. 583, pp. 124602-124602
Closed Access | Times Cited: 117
Application of machine learning techniques in groundwater potential mapping along the west coast of India
Pankaj Prasad, Victor J. Loveson, Mahender Kotha, et al.
GIScience & Remote Sensing (2020) Vol. 57, Iss. 6, pp. 735-752
Closed Access | Times Cited: 116
Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors
Seyed Amir Naghibi, Hossein Hashemi, Ronny Berndtsson, et al.
Journal of Hydrology (2020) Vol. 589, pp. 125197-125197
Closed Access | Times Cited: 112
Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater
Alireza Motevalli, Seyed Amir Naghibi, Hossein Hashemi, et al.
Journal of Cleaner Production (2019) Vol. 228, pp. 1248-1263
Closed Access | Times Cited: 108
Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh
Swades Pal, Sonali Kundu, Susanta Mahato
Journal of Cleaner Production (2020) Vol. 257, pp. 120311-120311
Closed Access | Times Cited: 108
Delineating groundwater potential zones of agriculture dominated landscapes using GIS based AHP techniques: a case study from Uttar Dinajpur district, West Bengal
Swagata Biswas, Bhabani Prasad Mukhopadhyay, Amit Bera
Environmental Earth Sciences (2020) Vol. 79, Iss. 12
Closed Access | Times Cited: 107
Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models
Wei Chen, Yang Li, Paraskevas Tsangaratos, et al.
Applied Sciences (2020) Vol. 10, Iss. 2, pp. 425-425
Open Access | Times Cited: 104
Assessment of groundwater potential zones using GIS and AHP techniques: a case study of the Lafia district, Nasarawa State, Nigeria
Stanley Ikenna Ifediegwu
Applied Water Science (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 104
Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping
Mahdi Boroughani, Sima Pourhashemi, Hossein Hashemi, et al.
Ecological Informatics (2020) Vol. 56, pp. 101059-101059
Closed Access | Times Cited: 98
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