Application of a multi-objective optimization model for the design of Piano Key Weirs with a fixed dam height

Azizi, Koorosh; Kashani, Ali R.; Ebrahimi, Saman; Jazaei, Farhad

doi:10.1139/cjce-2022-0096

Cited by 9 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This analysis can help identify the sources of uncertainty and improve the accuracy of the model predictions. Several methods and approaches have been developed for uncertainty analysis, including Monte Carlo simulation, binomial regression, and Bayesian averaging algorithm (Azizi et al, 2022;H. W. Chen et al, 2020;Forner et al, n.d.;Tafreshi et al, 2022;.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of hydrological variabilities of water quality models considering event-based scenarios: A case study

Ebrahimi,

Azizi,

Khorram

et al. 2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Given the significant momentum of developing water quality models to simulate water quality variables and support decision-making, the literature recognized the importance of addressing uncertainties embedded in the water quality models, such as inherent, parametric, and hydrological uncertainties. However, hydrological events' magnitude in terms of intensity has not been extensively scrutinized in previous studies. Hence, this paper aims to propose an adjusted Monte Carlo event-based scenarios framework that considers four scenarios (1-35 years of flow rate records, 2-events with ARI ≤ 10 years, 3events with ARI ≤ 5 years, 4events with ARI ≤ 1) to evaluate hydrological variabilities and quantify embedded uncertainties. The study employs a Qual2k model that simulates five water quality variables in the Zaroub river, Iran, as a case study. The model's uncertainty boundaries are quantified using five statistical metrics: Plevel, ARIL, SU, CU, and EU. The results of the study considering extreme hydrological events when examining the flow rate time histories leads to a significant increase in uncertainty in the water quality model.On the other hand, reducing the ARI values of hydrological events not only minimizes the uncertainty boundaries but also improves the accuracy of the model simulations. These findings highlight the crucial role of selecting hydrological scenarios based on the water quality variable under investigation. Furthermore, the proposed framework can be applied to any water quality model and water body. The study's outcomes suggest that the presented methodology reduces uncertainty and provides more reliable simulations for decision-making in water resources conundrums.

show abstract

Section: Introductionmentioning

confidence: 99%