Dynamic ANN Modeling for Flood Forecasting in a River Network. Roy, P., Choudhury, P. S., Saharia, M., Paruya, S., Kar, S., & Roy, S. Aip Conference Proceedings, 1298(1):219, 2010.
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Paper abstract bibtex
Paper abstract bibtex An experiment on predicting flood flows at each of the upstream and a down stream section of a river network is presented using focused Time Lagged Recurrent Neural Network with three different memories like TDNN memory, Gamma memory and Laguarre memory. This paper focuses on application of memory to the input layer of a TLRN in developing flood forecasting models for multiple sections in a river system. The study shows the Gamma memory has better applicability followed by TDNN and Laguarre memory.
@article{roy_dynamic_2010,
title = {Dynamic {ANN} {Modeling} for {Flood} {Forecasting} in a {River} {Network}},
volume = {1298},
url = {http://www.researchgate.net/profile/Manabendra_Saharia/publication/233802178_Dynamic_ANN_Modeling_for_Flood_Forecasting_in_a_River_Network/links/09e4150ba7be2900f2000000.pdf},
abstract = {An experiment on predicting flood flows at each of the upstream and a down stream
section of a river network is presented using focused Time Lagged Recurrent Neural Network with three different memories like TDNN memory, Gamma memory and Laguarre memory. This paper focuses on application of memory to the input layer of a TLRN in developing flood forecasting models for multiple sections in a river system. The study shows the Gamma memory has better applicability followed by TDNN and Laguarre memory.},
number = {1},
urldate = {2015-05-14},
journal = {Aip Conference Proceedings},
author = {Roy, Parthajit and Choudhury, P. S. and Saharia, Manabendra and Paruya, Swapan and Kar, Samarjit and Roy, Suchismita},
year = {2010},
pages = {219},
}Downloads: 0
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