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, continuity norm.

Forecasting of Flood Flow of Panam River Basin using Adaptive Neuro-Fuzzy Inference System (ANFIS) and ANN with Comparative Study

“…the comparative analysis of the models and their performance validation using diverse statistical criteria provide robust evidence of their potential in real-time scenarios. The paper underscores their applicability while ensuring adherence to the continuity norm, reinforcing their reliability and usefulness in practical settings [5]. Tabbussum and Qayoom carried out an analysis of the Jhelum River which is alluvial river in the area of the Indian Himalayas and various models were assessed and verified.…”

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

“…As fixed numbers of past samples are used as inputs, the network possesses a fixed or static memory. Most of the ANNbased flood forecasting models available in the literature are capable of providing forecasts at a single location and do not possess forecast updating capability (Agarwal et al 2021b). This restricts the applicability of these models in real-time situations (Coulibaly et al 2001).…”