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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:

Digital exploration of selected heavy metals using Random Forest and a set of environmental covariates at the watershed scale
Shohreh Moradpour, Mojgan Entezari, Shamsollah Ayoubi, et al.
Journal of Hazardous Materials (2023) Vol. 455, pp. 131609-131609
Closed Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Prediction of adsorption of metal cations by clay minerals using machine learning
Shoushi Zhao, Kai Chen, Beiyi Xiong, et al.
The Science of The Total Environment (2024) Vol. 924, pp. 171733-171733
Closed Access | Times Cited: 9
Predicting the distribution coefficient of cesium in solid phase groups using machine learning
Seok Min Hong, In-Ho Yoon, Kyung Hwa Cho
Chemosphere (2024) Vol. 352, pp. 141462-141462
Closed Access | Times Cited: 6
Fluoride Surveillance in a Fluoride Endemic Region Using Machine Learning Techniques: A Case Study of Vea Catchment, Upper East Region, Ghana
Musah Saeed Zango, Sidique Gawusu, Mahamuda Abu
Physics and Chemistry of the Earth Parts A/B/C (2025), pp. 103877-103877
Closed Access
Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands)
Víctor M. Jiménez, Jesús Santiago Notario del Pino, José Manuel Fernández-Guisuraga, et al.
Ecological Informatics (2025) Vol. 86, pp. 103054-103054
Open Access
A comparative study of empirical and machine learning approaches for soil thickness mapping in the Joshimath region (India)
Kunal Gupta, Neelima Satyam, Samuele Segoni
CATENA (2024) Vol. 241, pp. 108024-108024
Closed Access | Times Cited: 5
Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment.
François Nkinahamira, Anqi Feng, Lijie Zhang, et al.
TrAC Trends in Analytical Chemistry (2024), pp. 117980-117980
Closed Access | Times Cited: 5
Progress and prospects for remediation of soil potentially toxic elements pollution: A state-of-the-art review
Haixiang Zhang, Min Pu, Hongmei Li, et al.
Environmental Technology & Innovation (2024) Vol. 35, pp. 103703-103703
Open Access | Times Cited: 4
Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China
Kai Li, Guanghui Guo, Degang Zhang, et al.
Journal of Hazardous Materials (2024) Vol. 478, pp. 135454-135454
Closed Access | Times Cited: 4
Active learning-based random forest algorithm used for soil texture classification mapping in Central Vietnam
Nguyen Huu Ngu, Nguyễn Ngọc Thành, Trần Thanh Đức, et al.
CATENA (2023) Vol. 234, pp. 107629-107629
Closed Access | Times Cited: 12
Mapping global distributions of clay-size minerals via soil properties and machine learning techniques
Qinpeng Liao, Huangling Gu, Chongchong Qi, et al.
The Science of The Total Environment (2024) Vol. 949, pp. 174776-174776
Closed Access | Times Cited: 3
Urban land use simulation and carbon-related driving factors analysis based on RF-CA in Shanghai, China
L L Ye, Shuhe Zhao, Hong Yang, et al.
Ecological Indicators (2024) Vol. 166, pp. 112555-112555
Open Access | Times Cited: 3
Identifying driving factors of soil heavy metal at the mining area scale: Methods and practice
Jun Yang, Jingyun Wang, Chen Zhao, et al.
Chemosphere (2023) Vol. 350, pp. 140936-140936
Closed Access | Times Cited: 9
Geochemical characteristics, source analysis, influencing factors, and reserves of soil Selenium in Wuming, Guangxi, China
Jie Li, Xinyu Wang, Lei Wang, et al.
Environmental Geochemistry and Health (2024) Vol. 46, Iss. 7
Closed Access | Times Cited: 2
Quantifying the impact of factors on soil available arsenic using machine learning
Zhaoyang Han, Jun Yang, Yunxian Yan, et al.
Environmental Pollution (2024) Vol. 359, pp. 124572-124572
Closed Access | Times Cited: 2
Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling
Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, et al.
Smart Agricultural Technology (2024) Vol. 9, pp. 100559-100559
Open Access | Times Cited: 2
Retrieval of chromium and mercury concentrations in agricultural soils: Using spectral information, environmental covariates, or a fusion of both?
Long Wang, Zhou Yong, Sun Xiao, et al.
Ecological Indicators (2024) Vol. 167, pp. 112594-112594
Open Access | Times Cited: 2
Evaluating mobile applications for estimating soil properties: Quality of current apps, limitations and future directions
Ricky Sinclair, Sadia Sabrin Nodi, Muhammad Ashad Kabir
Computers and Electronics in Agriculture (2023) Vol. 216, pp. 108527-108527
Open Access | Times Cited: 7
Acid sulfate soil mapping in western Finland: How to work with imbalanced datasets and machine learning
Virginia Estévez, Stefan Mattbäck, Anton Boman, et al.
Geoderma (2024) Vol. 447, pp. 116916-116916
Open Access | Times Cited: 1
Improving forest age prediction performance using ensemble learning algorithms base on satellite remote sensing data
Jinjin Chen, Huaqiang Du, Fangjie Mao, et al.
Ecological Indicators (2024) Vol. 166, pp. 112327-112327
Open Access | Times Cited: 1
Improving prediction accuracy for acid sulfate soil mapping by means of variable selection
Virginia Estévez, Stefan Mattbäck, Anton Boman, et al.
Frontiers in Environmental Science (2023) Vol. 11
Open Access | Times Cited: 5
A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest
Xibo Xu, Zeqiang Wang, Xiaoning Song, et al.
Environmental Research (2023) Vol. 240, pp. 117570-117570
Closed Access | Times Cited: 5
A hierarchical residual correction-based hyperspectral inversion method for soil heavy metals considering spatial heterogeneity
Yulong Wang, Bin Zou, Sha Li, et al.
Journal of Hazardous Materials (2024) Vol. 479, pp. 135699-135699
Closed Access | Times Cited: 1
Quantitative expression of LNAPL pollutant concentrations in capillary zone by coupling multiple environmental factors based on random forest algorithm
Kexue Han, Rui Zuo, Donghui Xu, et al.
Journal of Hazardous Materials (2024) Vol. 479, pp. 135695-135695
Closed Access | Times Cited: 1
A geographically weighted neural network model for digital soil mapping of heavy metal copper in coastal cities
Yun Zeng, Tiezhu Shi, Qian Liu, et al.
Journal of Hazardous Materials (2024) Vol. 480, pp. 136285-136285
Open Access | Times Cited: 1
Prediction of lead in agricultural soils: An integrated approach using machine learning, terrain attributes and reflectance spectra
Prince Chapman Agyeman, Kingsley John, Ndiye Michael Kebonye, et al.
Pedosphere (2024)
Closed Access
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