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    <name>Article</name>
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              <text>A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence</text>
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              <text>Gated Recurrent Units (GRU); Long Short-Term Memory (LSTM)network; Multilayer Perception Layer (MLP) architecture; Precipitation; Recurrent Neural Network (RNN)</text>
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              <text>Flood occurs as often as possible happens due to many environmental changes in our planet in the present years. The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies. The various factors that play a major role in the occurrence of rainfall are rise in temperature, humidity level, dew point, pressure in and around the area of concern, wind speed, etc. In order to reduce the number of victims due to flood it is necessary to have a system to predict flood occurrence. In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results. In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors.  2020, Asian Research Association. All rights reserved.</text>
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              <text>Ullah T.F.; Gnana Prakasi O.S.; Kanmani P.</text>
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              <text>International Research Journal of Multidisciplinary Technovation, Vol-2, No. 5, pp. 8-14.</text>
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              <text>Asian Research Association</text>
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              <text>2020-01-01</text>
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              <text>&lt;a href="https://doi.org/10.34256/irjmt2052" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.34256/irjmt2052&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130881065&amp;amp;doi=10.34256%2Firjmt2052&amp;amp;partnerID=40&amp;amp;md5=a7e437201302286858f3e007e63e7c80" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130881065&amp;amp;doi=10.34256%2firjmt2052&amp;amp;partnerID=40&amp;amp;md5=a7e437201302286858f3e007e63e7c80&lt;/a&gt;</text>
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          <name>Rights</name>
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 25821040</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Ullah T.F., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Gnana Prakasi O.S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Kanmani P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India</text>
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