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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Senbagavalli, M | - |
dc.contributor.author | Sathiyamoorthi, V | - |
dc.contributor.author | Manju Bargavi, S K | - |
dc.contributor.author | Shekarappa, G Swetha | - |
dc.contributor.author | Jesudas, T | - |
dc.date.accessioned | 2023-12-18T09:45:34Z | - |
dc.date.available | 2023-12-18T09:45:34Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Chapter 3; pp. 29-44 | en_US |
dc.identifier.isbn | 9.78032E+12 | - |
dc.identifier.uri | https://doi.org/10.1016/B978-0-323-99503-0.00021-1 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2535 | - |
dc.description.abstract | Flooding 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.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Flood detection system | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Natural flood management | en_US |
dc.title | Deep Learning Model For Flood Estimate and Relief Management System Using Hybrid Algorithm | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | Book/ Book Chapters |
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