Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed, reconstructed and projected streamflow

Patskoski, Jason; Sankarasubramanian, A.

doi:10.1007/s00477-017-1456-2

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…Due to the wide range of space and time variabilities, river flow modeling has been a critical problem in water management and hydrology [1]. e operation of many water resources system rule curves needs monthly river flow forecasts [2].…”

Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

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Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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“…One limitation of sizing techniques, including the sequent peak algorithm, is the fact that they do not allow the association of the reservoir capacity to a return period. Their extreme dependence upon the available database frequently limits their application (Nunes & Pruski, 2015;Patskoski & Sankarasubramanian, 2018). In this sense, some initiatives have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sizing of annual water storage reservoirs considering return period association

et al. 2022

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An increasing concern among water resources managers is the search for ways to improve reservoir sizing techniques. A simple technical and scientifically based technique is the sequent peak method, which has been widely disseminated, but presents limitations with respect to the length of the available data series. The objective of this study was to propose modifications to the sequent peak method to overcome the limitations related to the impossibility of associating the reservoir storage capacity with return periods different from the length of the data series. Storage capacities were estimated for every year, considering gauge stations located in the Urucuia River Basin, based on the sequent peak method. In order to associate such capacities with a frequency factor (return period), the Gumbel distribution was applied to the estimated storage values. This association with return periods which differ from the number of years in the data series showed great applicability

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“…Silva, Portela, Naghettini, and Fernandes (2017) provide ideas for what could be used as prior information in extreme-frequency flood analyzes. Patskoski and Sankarasubramanian (2018) discuss various forms of potential prior information that are used in time-series-based hydrological studies, such as tree rings, sea surface temperature, probable maximum flood discharge (Fernandes et al, 2010), expert judgment (Viglione, Merz, Salinas, & Blöschl, 2013), and climate covariates (Sun, Thyer, Renard, & Lang, 2014). Other geologic hazards, such as tsunamis and landslides, can include prior information based on knowledge of seismotectonics (Geist & Uri, 2012), rainfall thresholds (Berti et al, 2012), and expert opinions (e.g., Yazdani & Kowsari, 2013).…”

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confidence: 99%

Bayesian inference in natural hazard analysis for incomplete and uncertain data

2019

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This study presents a method for estimating two area‐characteristic natural hazard recurrence parameters. The mean activity rate and the frequency–size power law exponent are estimated using Bayesian inference on combined empirical datasets that consist of prehistoric, historic, and instrumental information. The method provides for incompleteness, uncertainty in the event size determination, uncertainty associated with the parameters in the applied occurrence models, and the validity of event occurrences. This aleatory and epistemic uncertainty is introduced in the models through mixture distributions and weighted likelihood functions. The proposed methodology is demonstrated using a synthetic earthquake dataset and an observed tsunami dataset for Japan. The contribution of the different types of data, prior information, and the uncertainty is quantified. For the synthetic dataset, the introduction of model and event size uncertainties provides estimates quite close to the assumed true values, whereas the tsunami dataset shows that the long series of historic data influences the estimates of the recurrence parameters much more than the recent instrumental data. The conclusion of the study is that the proposed methodology provides a useful and adaptable tool for the probabilistic assessment of various types of natural hazards.

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Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed, reconstructed and projected streamflow

Cited by 4 publications

References 48 publications

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Optimization of sizing of annual water storage reservoirs considering return period association

Bayesian inference in natural hazard analysis for incomplete and uncertain data

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