Floods are natural disasters that affect the likelihood of occurrence. Forecasting and predicting floods have a significant role to play in ensuring that mitigation, adequate planning, and management can be carried out in advance. The Artificial Neural Network (ANN) is one of the techniques by which rapid forecasting and prediction can be carried out. In the present study, ANN has been used to simulate real-time floods in the lower Tapi basin. Data from the upstream gauging station of the reservoir, the inflow of the reservoir, and the downstream gauging site were simulated for three different events. The Feed–Forward network, the Levenberg Marquardt learning rule, and the Sigmoidal Axon transfer function are used in the models. Developed models have a correlation coefficient value close to one. The findings acquired from these models are satisfactory and the predicted flood discharge of the ANN is consistent with the observed values.


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    Title :

    Real-Time Flood Analysis Using Artificial Neural Network


    Additional title:

    Lecture Notes in Civil Engineering


    Contributors:


    Publication date :

    2020-09-28


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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