Flood forecasting and flood flow modeling in a river system using ANN

In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of...

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Auteurs principaux:	S. Agarwal, P. J. Roy, P. Choudhury, N. Debbarma
Format:	article
Langue:	EN
Publié:	IWA Publishing 2021
Sujets:	continuity equation gamma memory multiple input multiple output rmse storage Environmental technology. Sanitary engineering TD1-1066
Accès en ligne:	https://doaj.org/article/9371ce5520e54030b54765d432346ea0
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id	oai:doaj.org-article:9371ce5520e54030b54765d432346ea0
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spelling	oai:doaj.org-article:9371ce5520e54030b54765d432346ea02021-11-05T21:16:51ZFlood forecasting and flood flow modeling in a river system using ANN1751-231X10.2166/wpt.2021.068https://doaj.org/article/9371ce5520e54030b54765d432346ea02021-10-01T00:00:00Zhttp://wpt.iwaponline.com/content/16/4/1194https://doaj.org/toc/1751-231XIn terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of a storage parameter. Storage characteristics were presented implicitly and explicitly for various sections in a river system satisfying the continuity norm and mass balance flow. Furthermore, the multiple-input multiple-output (MIMO) model form having two base architectures, namely, MIMO-1 and MIMO-2, was accounted for learning fractional storage and actual storage variations and characteristics in a given model form. The model architecture was also obtained by using a trial-and-error approach, while the network architecture was acquired by employing gamma memory along with use of the multi-layer perceptron model form. Moreover, this paper discusses the comparisons and differences between both models. The model performances were validated using various statistical criteria, such as the root-mean-square error (whose value is less than 10% from the observed mean), the coefficient of efficiency (whose value is more than 0.90), and various other statistical parameters. This paper suggests applicability of these models in real-time scenarios while following the continuity norm. HIGHLIGHTS Applicability of Continuity equation while forecasting using ANN.; Use of storage variable in river flow prediction.; Routing type dynamic ANN models implication.; Use of MIMO (multiple input and multiple output) and MISO (multiple input and single output) model forms for forecasting approach.; Model is applicable and useful in real time flood scenarios.;S. AgarwalP. J. RoyP. ChoudhuryN. DebbarmaIWA Publishingarticlecontinuity equationgamma memorymultiple inputmultiple outputrmsestorageEnvironmental technology. Sanitary engineeringTD1-1066ENWater Practice and Technology, Vol 16, Iss 4, Pp 1194-1205 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	continuity equation gamma memory multiple input multiple output rmse storage Environmental technology. Sanitary engineering TD1-1066
spellingShingle	continuity equation gamma memory multiple input multiple output rmse storage Environmental technology. Sanitary engineering TD1-1066 S. Agarwal P. J. Roy P. Choudhury N. Debbarma Flood forecasting and flood flow modeling in a river system using ANN
description	In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of a storage parameter. Storage characteristics were presented implicitly and explicitly for various sections in a river system satisfying the continuity norm and mass balance flow. Furthermore, the multiple-input multiple-output (MIMO) model form having two base architectures, namely, MIMO-1 and MIMO-2, was accounted for learning fractional storage and actual storage variations and characteristics in a given model form. The model architecture was also obtained by using a trial-and-error approach, while the network architecture was acquired by employing gamma memory along with use of the multi-layer perceptron model form. Moreover, this paper discusses the comparisons and differences between both models. The model performances were validated using various statistical criteria, such as the root-mean-square error (whose value is less than 10% from the observed mean), the coefficient of efficiency (whose value is more than 0.90), and various other statistical parameters. This paper suggests applicability of these models in real-time scenarios while following the continuity norm. HIGHLIGHTS Applicability of Continuity equation while forecasting using ANN.; Use of storage variable in river flow prediction.; Routing type dynamic ANN models implication.; Use of MIMO (multiple input and multiple output) and MISO (multiple input and single output) model forms for forecasting approach.; Model is applicable and useful in real time flood scenarios.;
format	article
author	S. Agarwal P. J. Roy P. Choudhury N. Debbarma
author_facet	S. Agarwal P. J. Roy P. Choudhury N. Debbarma
author_sort	S. Agarwal
title	Flood forecasting and flood flow modeling in a river system using ANN
title_short	Flood forecasting and flood flow modeling in a river system using ANN
title_full	Flood forecasting and flood flow modeling in a river system using ANN
title_fullStr	Flood forecasting and flood flow modeling in a river system using ANN
title_full_unstemmed	Flood forecasting and flood flow modeling in a river system using ANN
title_sort	flood forecasting and flood flow modeling in a river system using ann
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/9371ce5520e54030b54765d432346ea0
work_keys_str_mv	AT sagarwal floodforecastingandfloodflowmodelinginariversystemusingann AT pjroy floodforecastingandfloodflowmodelinginariversystemusingann AT pchoudhury floodforecastingandfloodflowmodelinginariversystemusingann AT ndebbarma floodforecastingandfloodflowmodelinginariversystemusingann
_version_	1718443994883555328

Flood forecasting and flood flow modeling in a river system using ANN

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