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

Author:

Agarwal S.1ORCID,Roy P. J.1,Choudhury P.1ORCID,Debbarma N.2ORCID

Affiliation:

1. Department of Civil Engineering, National Institute of Technology Silchar, NIT Road, Silchar, Assam 788010, India

2. Department of Civil Engineering, National Institute of Technology Agartala, Barjala, Jirania, Agartala, Tripura 799046, India

Abstract

Abstract 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.

Publisher

IWA Publishing

Subject

Water Science and Technology

Reference27 articles.

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