Open Access
| Issue |
E3S Web Conf.
Volume 687, 2026
The 2nd International Conference on Applied Sciences and Smart Technologies (InCASST 2025)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 11 | |
| Section | Environmental Developments & Sustainable Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202668701002 | |
| Published online | 15 January 2026 | |
- R. Chaysiri, J. Sukruay, Sustainability analysis for the lower Chao Phraya River using system dynamics. J. Res. Unit Sci. Tech. Environ. Learning. 12, 105 (2021). https://doi.org/10.14456/jstel.2021.8 [Google Scholar]
- J. Krohkaew, P. Nilaphruek, N. Witthayawiroj, S. Uapipatanakul, Y. Thwe, P. N. Crisnapati, Thailand raw water quality dataset analysis and evaluation. Data (Basel) 8, 141 (2023). https://doi.org/10.3390/data8090141 [Google Scholar]
- B. X. Lee, F. Kjaerulf, S. Turner, L. Cohen, P. D. Donnelly, R. Muggah, R. Davis, A. Realini, B. Kieselbach, L. S. MacGregor, I. Waller, R. Gordon, M. Moloney-Kitts, G. Lee, J. Gilligan, Transforming our world: Implementing the 2030 agenda through sustainable development goal indicators. J Public Health Pol 37(Suppl1), 13–31 (2016). https://doi.org/10.1057/s41271-016-0002-7 [Google Scholar]
- S. N. Zainurin, W. Z. Wan Ismail, S. N. I. Mahamud, I. Ismail, J. Jamaludin, K. N. Z. Ariffin, W. M. Wan Ahmad Kamil, Advancements in monitoring water quality based on various sensing methods: a systematic review. Int J Environ Res Public Health. 19 (21), 14080 (2022). https://doi.org/10.3390/ijerph192114080 [Google Scholar]
- World Health Organization, Drinking-water. World health organization [online]. (2023). https://www.who.int/news-room/fact-sheets/detail/drinking-water [Google Scholar]
- P. Jayaraman, K. K. Nagarajan, P. Partheeban, and V. Krishnamurthy, Critical review on water quality analysis using IoT and machine learning models. IJIM Data Insights. 4, 100210 (2024). https://doi.org/10.1016/j.jjimei.2023.100210 [Google Scholar]
- M. A. Rahu, A. F. Chandio, K. Aurangzeb, S. Karim, M. Alhussein, M. S. Anwar, Toward design of internet of things and machine learning-enabled frameworks for analysis and prediction of water quality. IEEE Access. vol. 11, 101055 (2023). https://doi.org/10.1109/ACCESS.2023.3315649 [Google Scholar]
- A. Fernández del Castillo, C. Yebra-Montes, M. Verduzco Garibay, J. de Anda, A. Garcia-Gonzalez, and M. S. Gradilla-Hernández, Simple prediction of an ecosystem-Specific Water Quality Index and the Water Quality Classification of a Highly Polluted River through Supervised Machine Learning. Water. 14, 1235 (2022). https://doi.org/10.3390/w14081235 [Google Scholar]
- S. Phitaktim, C. Wangka-orm, S. Wesetsri, T. Togari, S. Sato, M. Wongaree, Assessment of surface water quality of Huai Luang River in Udon Thani, Thailand, using the water quality index. Int. J. Environ. Sci. Dev. 14, 381 (2023). https://doi.org/10.18178/ijesd.2023.14.6.1458 [Google Scholar]
- D. P. Nguyen, H. D. Ha, N. T. Trinh, M. T. Nguyen, Application of artificial intelligence for forecasting surface quality index of irrigation systems in the Red River Delta, Vietnam. Environ Syst Res. 12, 24 (2023). https://doi.org/10.1186/s40068-023-00307-6 [Google Scholar]
- M. Hmoud Al-Adhaileh, F. Waselallah Alsaade, Modelling and prediction of water quality by using artificial intelligence. Sustainability. 13, 4259 (2021). https://doi.org/10.3390/su13084259 [CrossRef] [Google Scholar]
- J. Krohkaew, P. Nilaphruek, N. Witthayawiroj, S. Uapipatanakul, Y. Thwe, P. N. Crisnapati, RAW-Q: Raw Water Quality Dataset, Thailand. IEEE Dataport (2023). https://doi.org/10.21227/3q8d-jw96 [Google Scholar]
- S. Hochreiter, J. Schmidhuber, Long Short-Term Memory. Neural Comput. 9, 1735 (1997). https://doi.org/10.1162/neco.1997.9.8.1735 [CrossRef] [PubMed] [Google Scholar]
- K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, J. Schmidhuber, LSTM: A search space Odyssey. IEEE Trans Neural Netw Learn Syst. 28, 2222 (2017). https://doi.org/10.1109/TNNLS.2016.2582924 [CrossRef] [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.