Water Quality Monitoring Using IoT & Machine Learning
| dc.contributor.author | Omambia Andrew , Maake Benard & Wambua Anthony | |
| dc.date.accessioned | 2024-05-28T09:49:19Z | |
| dc.date.available | 2024-05-28T09:49:19Z | |
| dc.date.issued | 2022 | |
| dc.description | Conference Proceeding | |
| dc.description.abstract | Safe water access is fundamental form of human survival and it is presented as a fundamental human right. As consumers use water, primarily sourced from pipes and springs located around towns, contamination, leakages, and pilferage happen. IoT and Machine Learning offer a promising solution to address these challenges. Premised on these technologies, the authors propose a system that monitors water quality and pilferage and wastage that uses machine learning algorithms for decision making. | |
| dc.identifier.citation | Omambia A. , Maake B. & Wambua A. (2022): Water Quality Monitoring Using IoT & Machine Learning. : IST-Africa 2022 Conference Proceedings. | |
| dc.identifier.isbn | 978-1-905824-69-4 | |
| dc.identifier.uri | https://repository.daystar.ac.ke/handle/123456789/4659 | |
| dc.language.iso | en | |
| dc.publisher | IST-Africa 2022 Conference Proceedings | |
| dc.subject | Water quality monitoring | |
| dc.subject | Internet of things | |
| dc.subject | machine learning | |
| dc.subject | smart cities | |
| dc.subject | IoT | |
| dc.title | Water Quality Monitoring Using IoT & Machine Learning | |
| dc.type | Other |
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