Predictive Model for Enhancing Water Quality Monitoring leveraging Satellite Data

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dc.contributor.authorPrakash, P.
dc.contributor.authorSowmya Kamath, S.
dc.contributor.authorBhattacharjee, S.
dc.contributor.authorUmesh, P.
dc.contributor.authorGangadharan, K.V.
dc.date.accessioned2026-02-06T06:33:56Z
dc.date.issued2024
dc.description.abstractRemote sensing data can be used instead of conventional methods to collect image data from multiple satellites with acceptable spatial and temporal coverage. The proposed study makes use of Landsat 8 Operational Land Imager (OLI) data. The relationship between reflectance retrieved from Landsat 8 OLI data and in-situ data is established through the application of machine learning model. The dataset is made up of Landsat8 band extractions for water quality features. Water with high turbidity is predicted and verified using in-situ data that was gathered within the chosen temporal and spatial limits. © 2024 IEEE.
dc.identifier.citation2024 IEEE Space, Aerospace and Defence Conference, SPACE 2024, 2024, Vol., , p. 161-164
dc.identifier.urihttps://doi.org/10.1109/SPACE63117.2024.10667767
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/28938
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectlandsat OLI imagery
dc.subjectmachine learning
dc.subjectturbidity
dc.subjectWater quality monitoring
dc.titlePredictive Model for Enhancing Water Quality Monitoring leveraging Satellite Data

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