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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:

Machine learning and explainable AI for chlorophyll-a prediction in Namhan River Watershed, South Korea
Ji Woo Han, TaeHo Kim, Sangchul Lee, et al.
Ecological Indicators (2024) Vol. 166, pp. 112361-112361
Open Access | Times Cited: 11

Showing 11 citing articles:

Unveiling the Hidden Connections: Using Explainable Artificial Intelligence to Assess Water Quality Criteria in Nine Giant Rivers
Sourav Kundu, P. K. Datta, Puja Pal, et al.
Journal of Cleaner Production (2025), pp. 144861-144861
Closed Access | Times Cited: 2
Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study
Bekir Zahit Demiray, Omer Mermer, Özlem Baydaroğlu, et al.
Water (2025) Vol. 17, Iss. 5, pp. 676-676
Open Access | Times Cited: 1
Analysis of cultivated land degradation in southern China: diagnostics, drivers, and restoration solutions
Yanqing Liao, Zhihong Yu, Lihua Kuang, et al.
Frontiers in Plant Science (2025) Vol. 16
Open Access
Developing a real-time water quality simulation toolbox using machine learning and application programming interface
Gi-Hun Bang, Na-Hyeon Gwon, Min‐Jeong Cho, et al.
Journal of Environmental Management (2025) Vol. 377, pp. 124719-124719
Closed Access
Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie
Yang Song, Chunqi Shen, Hong Yi
Journal of Environmental Management (2025) Vol. 380, pp. 125007-125007
Closed Access
Water quality parameters-based prediction of dissolved oxygen in estuaries using advanced explainable ensemble machine learning
Xingda Chen, Chenyao Zhao, Jinyue Chen, et al.
Journal of Environmental Management (2025) Vol. 380, pp. 125146-125146
Closed Access
Improved prediction of chlorophyll-a concentrations using advancing graph neural network variants
Sung-Hyun Yoon, Kuk-Hyun Ahn
The Science of The Total Environment (2025) Vol. 979, pp. 179481-179481
Closed Access
TRIX-XGBoost Trophic Model for assessing the trophic status of Ichkeul Lake, a Ramsar sanctuary in Northern Tunisia
Sabrine Sahbani, Béchir Béjaoui, Afef Fathalli, et al.
Marine Pollution Bulletin (2025) Vol. 217, pp. 118148-118148
Closed Access
Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea
Byeongwon Lee, Jong Kwon Im, Ji Woo Han, et al.
Environmental Science and Pollution Research (2024)
Closed Access | Times Cited: 3
Estimation of chlorophyll-a in uncrewed aircraft systems imagery using autonomous surface vessel data with machine learning algorithms and feature selection techniques
Mohammad Shakiul Islam, Padmanava Dash, Abduselam M. Nur, et al.
Ecological Informatics (2024), pp. 102954-102954
Open Access | Times Cited: 2
Identifying factors influencing reservoir eutrophication using interpretable machine learning combined with shoreline morphology and landscape hydrological features: A case study of Danjiangkou Reservoir, China
Chenyi Shi, Nana Zhuang, Yiheng Li, et al.
The Science of The Total Environment (2024) Vol. 951, pp. 175450-175450
Closed Access
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