River flow forecasting by comparative analysis of multiple input and multiple output models form using ANN

Abstract

ANN was used to create a storage-based concurrent flow forecasting model. River flow parameters in an unsteady flow must be modeled using a model formulation based on learning storage change variable and instantaneous storage rate change. Multiple input-multiple output (MIMO) and multiple input-single output (MISO models in three variants were used to anticipate flow rates in the Tar River Basin in the United States. Gamma memory neural networks, as well as MLP and TDNNs models, are used in this study. When issuing a forecast, storage variables for river flow must be considered, which is why this study includes them. While considering mass balance flow, the proposed model can provide real-time flow forecasting. Results obtained are validated using various statistical criteria such as RMS error and coefficient of correlation. For the models, a coefficient of correlation value of more than 0.96 indicates good results. While considering the mass balance flow, the results show flow fluctuations corresponding to expressly and implicitly provided storage variations.

Comparative study on stream flow prediction using the GMNN and wavelet-based GMNN

“…Based on the literature survey, Agarwal et al (2021a) computed storage rate change implicitly. With this in context, storage rate change has been computed explicitly in this work, satisfying the continuity norm.…”

“…Equations ( 2), ( 5), and (6) reformulate the models using depth variables (Agarwal et al 2021a). That is, by employing Equation (8) in conjunction with Equations ( 5) and ( 6), the 'end of the period' flow depth for the upstream and downstream stations can be written as a function (Choudhury 2007;Choudhury & Sankarasubramanian 2009) of the current flow depths at the upstream and downstream stations as given in Equations ( 9) and (10).…”