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.
The detection of appropriate homogeneous regions is an important step in regional frequency analysis with the determination of homogeneity depending to a great extent on the type of method used in grouping. So, the study considers a genetic-algorithm-based clustering method to identify homogeneous precipitation regions for 39 gauge stations of the north-eastern region of India. The performance evaluation is done using six cluster validation measures. Further, considering all the six indices together, selection for the optimum cluster is modelled as a multi-criteria decision making (MCDM) problem. Three MCDM methods, namely TOPSIS, WASPAS and VIKOR, are applied to obtain ranked clusters which are then subjected to a heterogeneity test using the L-moments approach. The results suggested the stations to be grouped into three homogeneous regions. Comparison with the k-means method indicated relatively better performance for genetic-algorithm-based clustering. Finally, an L-moment ratio diagram and goodness-of-fit measures were conducted to select regional frequency distributions for the identified homogeneous regions.
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.
Non availability of adequate extreme rainfall information at any place of interest are solved using regionalization where subjective grouping of similar attributes of nearby gauged stations is performed. K-Means and Fuzzy C-Means are commonly used methods in regionalization of rainfall, but application of genetic algorithm is very rarely explored. Genetic algorithms (GA) are highly efficient evolutionary algorithms, and through an appropriate objective function can effectively achieve the purpose of clustering. In the present study, Davies-Bouldin index is considered and validation is performed using a set of validation measures. Taking into account the varied output obtained in each validation measure, an ensembled approach involving multi criteria decision making is applied to obtain optimal ranked solutions, and the procedure is extended to K-Means and Fuzzy C-Means for comparision. From the results obtained, GA based clustering is found to outperform other two algorithms in formation of homogenous regions with better performances in leave-one-out cross validation (LOOCV) test and sensitivity analysis. Accuracy of regional growth curves of regions assessed using regional relative bias and RMSE suggest low uncertainty and accurate quantile estimates in GA regions. Further, information transfer index based on entropy evaluated among GA regions is found to be highest and K-Means lowest.
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
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.