River Runoff Forecasting Model Using Time lagged Artificial Neural Network |
Author(s): |
| Manish Dubey , Mewar University, Rajsthan, India; Dr. Sudhir Nigam, TIT, Bhopal, M.P. India |
Keywords: |
| Rainfall, Runoff, Forecasting, Artificial Neural Network, Time Lagged Neural Network |
Abstract |
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In this paper Narmada river, of subtropical region in India is chosen for flood forecast modeling with Artificial neural network. The modeling methods were mainly focused to forecast magnitudes of river runoff (trend and magnitude) for future time intervals. The Time Lagged Neural Network (TLNN) models are designed with two types of data set. In the first type of TLNN design the output layer is fed only by the runoff values that is univariate data set where only the past runoff values of 5, 5 and 10 years are taken into consideration (univariate input). While in second types of TLNN design the output is made dependent of two (multivariate) input layers of rainfall and the runoff. In this paper the prediction of river runoff found flourishing and relevant through ANN methods particularly in case of subtropical Indian rivers, which are mainly having monsoon dependent runoff. |
Other Details |
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Paper ID: IJSRDV8I60033 Published in: Volume : 8, Issue : 6 Publication Date: 01/09/2020 Page(s): 296-301 |
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