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.

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.

Estimation of rainfall quantile is an important step in regional frequency analysis for planning and design of any water resources project. Related evaluations of accuracy and uncertainty help to further assist in enhancing the reliability of design estimates. In this study, therefore, we investigate the accuracy and uncertainty of regional frequency analysis of extreme rainfall computed from genetic algorithm-based clustering. Uncertainty assessment is explored with prediction of quantiles with a new spatial Information Transfer Index (ITI) and Monte Carlo simulation framework. And, accuracy assessment is done with the comparison of regional growth curves to at-site analysis for each homogenous region. Further, uncertainty assessment with the ITI method is compared with Maximum Likelihood estimation (MLE) optimized by a genetic algorithm (GA) to check the suitability of the method. Results obtained suggest the ITI-based uncertainty assessment for regional estimates outperformed those of at-site estimates. The MLE-GA method based on at-site estimates was found to be better than at-site estimates based on L-moments, suggesting the former as a better alternative to compare with regional frequency estimates. Moreover, minimal bias and least deviation of the regional growth curve were obtained in the rainfall regions. The confidence intervals of regional estimates were seen to be well within the bounds of normality assumptions. Doi: 10.28991/cej-2021-03091762 Full Text: PDF

Flood flow forecast is essential for mitigating damages in flood-prone areas all over the world. Advanced actions and methodology to optimize peak flow criteria can be adopted based on forecasted discharge information. This paper applied the models of the integrated wavelet, multilayer perceptron (MLP), time-delay neural network (TDNN), and gamma memory neural network (GMNN) to predict hourly river-level fluctuations, including storage rate change variable. Accordingly, the researchers initially used the discrete wavelet transform to decompose the water discharge time-series into low- and high-frequency components. After that, each component was separately predicted by using the MLP, TDNN, and GMNN models. The performance of the proposed models, namely wavelet–MLP, wavelet–TDNN, and wavelet–GMNN, was compared with that of single MLP, TDNN, and GMNN models. This analysis affirms that precision is better in the case of integrated models for forecasting river reach levels in the study region. Furthermore, multiple inputs–multiple outputs (MIMO) networks (MIMO-1 artificial neural network (ANN) and MIMO-2 ANN), along with multiple inputs–single output (MISO) ANN were employed for obtaining flow forecasts for several sections in a river basin. Model performances were also evaluated by using the root mean squared error having less than 10% of the average mean value, with the coefficient of correlation being more than 0.91 and with the peak flow criteria showing the chances of flash floods being low to moderate and of values not more than 0.15.

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