University of Bahrain
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Adaptive IoT Enabled Flood Management System: An Artificial Intelligence Approach

Show simple item record Rai Goyal, Himanshu Kumar Ghanshala, Kamal Sharma, Sachin 2022-08-06T19:42:37Z 2022-08-06T19:42:37Z 2022-08-06
dc.identifier.issn 2210-142X
dc.description.abstract Flood control is one of the most important areas where practical measures have been implemented to minimise damage. We are developing an IoT-enabled adaptive AI technique for efficient fluid management in this research project. In recent years, floods in Kerala, India, have caused significant damage to people, infrastructure, and the environment. Precipitation rates, temperature, and other data were collected by various IoT devices. Data on floods in Kerala is collected and sorted into two categories: 80% for training and 20% for testing. Six different machine learning models are trained, and the proposed hybrid model achieves a maximum accuracy of 99.64%. We were able to avoid losses as a result of integration. en_US
dc.language.iso en en_US
dc.publisher University Of Bahrain en_US
dc.subject Artificial Intelligence en_US
dc.subject Flood management en_US
dc.subject IoT en_US
dc.subject Kerala floods en_US
dc.subject Machine Learning en_US
dc.title Adaptive IoT Enabled Flood Management System: An Artificial Intelligence Approach en_US
dc.volume 12 en_US
dc.issue 1 en_US
dc.pagestart 607 en_US
dc.pageend 617 en_US
dc.contributor.authorcountry India en_US
dc.contributor.authoraffiliation Department of Computer Science aand Engineering, Graphic Era (Deemed to be University), Dehradun en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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