Water Quality Monitoring Using IoT & Machine Learning

Simple item page
dc.contributor.authorOmambia, Andrew
dc.contributor.authorMaake, Benard
dc.contributor.authorWambua, Anthony
dc.date.accessioned2024-07-30T05:38:25Z
dc.date.available2024-07-30T05:38:25Z
dc.date.issued2022
dc.descriptionJournal Article
dc.description.abstractAbstract: 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.description.sponsorshipDaystar University
dc.identifier.citationOmambia, Andrew., Maake, Benard., & Wambua, Anthony., (2022)., Water Quality Monitoring Using IoT & Machine Learning., ST-Africa.org/Conference2022.
dc.identifier.issn978-1-905824-69-4
dc.identifier.urihttps://repository.daystar.ac.ke/handle/123456789/4931
dc.language.isoen
dc.publisherST-Africa.org/Conference2022
dc.subjectWater quality monitoring
dc.subjectInternet of things
dc.subjectmachine learning
dc.subjectsmart cities
dc.subjectIoT
dc.titleWater Quality Monitoring Using IoT & Machine Learning
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Water Quality Monitoring Using IoT & Machine Learning.pdf
Size:
1.03 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Journal Articles