Open Access
| Issue |
BIO Web Conf.
Volume 238, 2026
VI International Scientific and Practical Conference “Ensuring Sustainable Development in the Context of Agriculture, Energy, Ecology and Earth Science” (ESDCA 2026)
|
|
|---|---|---|
| Article Number | 02010 | |
| Number of page(s) | 10 | |
| Section | Ecology and Conservation of Biological Diversity | |
| DOI | https://doi.org/10.1051/bioconf/202623802010 | |
| Published online | 10 June 2026 | |
- United Nations, Sustainable Development Goal 6: Ensure Availability and Sustainable Management of Water and Sanitation for All, URL: https://sdgs.un.org/goals/goal6 [Google Scholar]
- World Health Organization (WHO), Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First Addendum (WHO, Geneva, 2017), URL: https://www.who.int/publications/i/item/9789241549950 [Google Scholar]
- K.G. Liakos, P. Busato, D. Moshou, S. Pearson, D. Bochtis, Machine learning in agriculture: a review, Sensors 18(8), 2674 (2018). DOI: 10.3390/s18082674 [CrossRef] [Google Scholar]
- F. Garda-Âvila, A. Avilés-Anazco, J. Ordonez-Jara, C. Guanuchi-Quintero, L. Flores del Pino, L. Ramos-Fernandez, Application of machine learning techniques for the prediction of water quality index in an Andean city, Environ. Monit. Assess. 195(1), 198 (2023). DOI: 10.1007/s10661-022-10806-1 [Google Scholar]
- M.A. Wani, F.A. Bhat, S. Afzal, A.I. Khan, Advances in Deep Learning (Springer, Singapore, 2020). DOI: 10.1007/978-981-13-6794-6 [Google Scholar]
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. (Springer, New York, 2009), URL: https://hastie.su.domains/ElemStatLearn/ [Google Scholar]
- L. Breiman, Random forests, Mach. Learn. 45(1), 5–32 (2001). DOI: 10.1023/A:1010933404324 [Google Scholar]
- Kaggle, Water Quality Dataset, URL: https://www.kaggle.com/datasets/adityakadiwal/water-potability [Google Scholar]
- W. McKinney, Data structures for statistical computing in Python, in Proceedings of the 9th Python in Science Conference (SciPy2010) (2010), pp. 56–61. DOI: 10.25080/Majora-92bf1922-00a [Google Scholar]
- G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning: With Applications in R (Springer, New York, 2013) [Google Scholar]
- F. Pedregosa, G. Varoquaux, A. Gramfort et al., Scikit-learn: machine learning in Python, J. Mach. Learn. Res. 12, 2825–2830 (2011) [MathSciNet] [Google Scholar]
- M. Ali, PyCaret: An Open Source, Low-Code Machine Learning Library in Python, PyCaret Version 3.0.0 (2023) [Google Scholar]
- M. Sokolova, G. Lapalme, A systematic analysis of performance measures for classification tasks, Inf. Process. Manag. 45(4), 427–437 (2009). DOI: 10.1016/j.ipm.2009.03.002 [Google Scholar]
- A. Singh, A.K. Pandey, Water potability classification using machine learning algorithms, in Proceedings of the International Conference on Innovative Computing and Communications (ICICC), 1–9. (2022) [Google Scholar]
- L. Chen, H. Wang, H. Li, A comparative study of machine learning models for predicting water potability, Water 15(5), 987 (2023). DOI: 10.3390/w15050987 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.