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- W3157184426 abstract "River flooding may be a phenomenon which will have a devastating result on human life and economic losses. There are numerous approaches in learning river flooding; but, insufcient understanding and restricted data concerning flooding conditions hinder the event of bar and management measures for this phenomenon. a replacement approach for the prediction of water level in association with flood severity victimization the ensemble model. Our approach leverages the latest developments within the net of Things (IoT) and machine learning for the automated analysis of flood knowledge which may be helpful to stop natural disasters. Research outcomes indicate that ensemble learning provides a a lot of reliable tool to predict flood severity levels. In learning victimization the Long-Short Term memory model and random forest.It outperformed individual models with a sensitivity, specicity and accuracy of 71.4%, 85.9%, 81.13%, severally." @default.
- W3157184426 created "2021-05-10" @default.
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- W3157184426 date "2021-01-01" @default.
- W3157184426 modified "2023-10-02" @default.
- W3157184426 title "FLOOD PREDICTION USING MACHINE LEARNING ALGORITHMS" @default.
- W3157184426 hasPublicationYear "2021" @default.
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