Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2535
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dc.contributor.authorSenbagavalli, M-
dc.contributor.authorSathiyamoorthi, V-
dc.contributor.authorManju Bargavi, S K-
dc.contributor.authorShekarappa, G Swetha-
dc.contributor.authorJesudas, T-
dc.date.accessioned2023-12-18T09:45:34Z-
dc.date.available2023-12-18T09:45:34Z-
dc.date.issued2023-
dc.identifier.citationChapter 3; pp. 29-44en_US
dc.identifier.isbn9.78032E+12-
dc.identifier.urihttps://doi.org/10.1016/B978-0-323-99503-0.00021-1-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2535-
dc.description.abstractFlooding is a major geographical calamity that occurs frequently in some countries and infrequently in others. It is critical to remain vigilant and make early precautions to prevent unnecessary threats that endanger both person and property. In this chapter, we have focused on deep learning model in which the affected drainage can be found and alert people who take necessary precautions based on it. In this chapter, we present in-depth details of the applications of the prior techniques in flood estimate and relief management system. © 2023 Elsevier Inc. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectArtificial intelligenceen_US
dc.subjectDeep learningen_US
dc.subjectFlood detection systemen_US
dc.subjectMachine learningen_US
dc.subjectNatural flood managementen_US
dc.titleDeep Learning Model For Flood Estimate and Relief Management System Using Hybrid Algorithmen_US
dc.typeBook chapteren_US
Appears in Collections:Book/ Book Chapters

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