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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:

BOD5 prediction using machine learning methods
Kai Sheng Ooi, Zhiyuan Chen, Phaik Eong Poh, et al.
Water Science & Technology Water Supply (2021) Vol. 22, Iss. 1, pp. 1168-1183
Open Access | Times Cited: 15

Showing 15 citing articles:

Second-order based ensemble machine learning technique for modelling river water biological oxygen demand (BOD): Insights into improved learning
A. G. Usman, May Almousa, Hanita Daud, et al.
Journal of Radiation Research and Applied Sciences (2025) Vol. 18, Iss. 2, pp. 101439-101439
Closed Access
Enhancing BOD5 Forecasting Accuracy with the ANN-Enhanced Runge Kutta Model
Rana Muhammad Adnan, Ahmed A. Ewees, Mo Wang, et al.
Journal of environmental chemical engineering (2025), pp. 115430-115430
Closed Access
Integrated machine learning-based optimization framework for surface water quality index comparing coastal and non-coastal cases of Guangxi, China
Xizhi Nong, Fankang He, Lihua Chen, et al.
Marine Pollution Bulletin (2025) Vol. 213, pp. 117564-117564
Closed Access
Enhancing water quality prediction: a machine learning approach across diverse water environments
Sabanaz S. Peerzade, Pooja Kamat
Water quality research journal. (2025)
Open Access
Development of local and global wastewater biochemical oxygen demand real-time prediction models using supervised machine learning algorithms
Abdulaziz Sami Qambar, Mohammed Majid M. Al-Khalidy
Engineering Applications of Artificial Intelligence (2022) Vol. 118, pp. 105709-105709
Open Access | Times Cited: 18
Real-Time Water Quality Assessment via IoT: Monitoring pH, TDS, Temperature, and Turbidity
Wibowo Harry Sugiharto, Heru Susanto, Agung Budi Prasetijo
Ingénierie des systèmes d information (2023) Vol. 28, Iss. 4, pp. 823-831
Open Access | Times Cited: 10
Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks
Juan Huan, Jialong Yuan, Hao Zhang, et al.
Water Science & Technology (2024) Vol. 89, Iss. 8, pp. 1961-1980
Open Access | Times Cited: 3
Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators
Suyog Gupta, Sunil Kumar Gupta
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 19, pp. 27829-27845
Closed Access | Times Cited: 2
Improving the statistical downscaling performance of climatic parameters with convolutional neural networks
Aida Hosseini Baghanam, Vahid Nourani, Mohammad Mahdi Bejani, et al.
Journal of Water and Climate Change (2024) Vol. 15, Iss. 4, pp. 1772-1796
Open Access | Times Cited: 2
Predictive modeling of BOD throughout wastewater treatment: a generalizable machine learning approach for improved effluent quality
Offir Inbar, Moni Shahar, Dror Avisar
Environmental Science Water Research & Technology (2024) Vol. 10, Iss. 10, pp. 2577-2588
Closed Access | Times Cited: 2
Evaluation of Machine Learning Predictions of a Highly Resolved Time Series of Chlorophyll-a Concentration
Felipe de Luca Lopes de Amorim, Johannes Rick, Gerrit Lohmann, et al.
Applied Sciences (2021) Vol. 11, Iss. 16, pp. 7208-7208
Open Access | Times Cited: 16
Performance Evaluation of Artificial Intelligence Methods Predicting Annual Number of Patients in Hospitals
Young‐Taek Park, Seon Min Lee, Yul Hee Lee, et al.
Health Insurance Review & Assessment Service Research (2024) Vol. 4, Iss. 1, pp. 73-86
Open Access | Times Cited: 1
Data imputation of water quality parameters through feed-forward neural networks
Luís Otávio Miranda Peixoto, Bárbara Alves de Lima, Camila de Carvalho Almeida, et al.
RBRH (2023) Vol. 28
Open Access | Times Cited: 2
Using multiple linear regression for biochemical oxygen demand prediction in water
Isaiah Kiprono Mutai, Kristof Van Laerhoven, Nancy W. Karuri, et al.
Applied Computing and Intelligence (2024) Vol. 4, Iss. 2, pp. 125-137
Closed Access
Waste stabilization pond modelling using extreme gradient boosting machines
Nkpa Ogarekpe, Jonah Chukwuemeka Agunwamba, I.T. Tenebe, et al.
Water Practice & Technology (2024) Vol. 19, Iss. 11, pp. 4572-4584
Closed Access
Smart Drying with Machine Learning Methods
Nicholas Li Jian Chandra, Zhiyuan Chen, Chung Lim Law
(2024), pp. 27-33
Closed Access
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