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

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. 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.;

Keywords

• continuity equation
• gamma memory
• multiple input
• multiple output
• rmse
• storage