A robust decision-making framework to improve reservoir water quality using optimized selective withdrawal strategies

Nikoo, Mohammad Reza, Barhami, Nafiseh, Madani, Kaveh, Al-Rawas, Ghazi, Vanda, Sadegh and Nazari, Rouzbeh, (2024). A robust decision-making framework to improve reservoir water quality using optimized selective withdrawal strategies. Journal of Hydrology, 635 131153-n/a

Document type:
Article
Collection:
UNU-INWEH Journal Articles

Metadata
Links
Versions
Statistics
  • Sub-type Journal article
    Author Nikoo, Mohammad Reza
    Barhami, Nafiseh
    Madani, Kaveh
    Al-Rawas, Ghazi
    Vanda, Sadegh
    Nazari, Rouzbeh
    Title A robust decision-making framework to improve reservoir water quality using optimized selective withdrawal strategies
    Appearing in Journal of Hydrology
    Volume 635
    Publication Date 2024-04-03
    Place of Publication Amsterdam, Netherlands
    Publisher Elsevier
    Start page 131153
    End page n/a
    Language eng
    Abstract In arid locations such as Oman, improving the quality of water released from a reservoir can be daunting. This is particularly true for Wadi Dayqah Dam, which struggles to provide sufficient, high-quality water for downstream needs. The study's objective was to establish more reliable operational guidelines, as well as identify vulnerable outlets and release ratios. To do so, a multi-objective optimization algorithm (NSGA-II) was combined with a selective withdrawal approach, using CE-QUAL-W2 and an Artificial Neural Network to simulate water quality in a reservoir. Then, a Robust Decision Making (RDM) approach was utilized to diagnose solutions that maintain acceptable water quality according to users’ requirements. Finally, scenario discovery models (Patient Rule Induction Method (PRIM) and Classification and Regression Tree (CART)) were used to calculate vulnerable outlets and release ratios based on the established threshold. The study adopted an XLRM framework to manage a proposed model under climate change scenarios and specify the effects of involved factors. XLRM stands for eXogenous factors, Levers used by the policy-makers, Relationships between L and X, and Measurement matrix to evaluate scenarios. This study found that a higher-quality reservoir release correlated with more activated outlets and decreased pollutant concentration. Evaluating robust solutions showed that vulnerability increased under scenarios with lower annual rainfall, and water released from low and mid-level outlets was more vulnerable than that from the surface. To enhance operational stability and reduce vulnerability, the suggestions advocate for a strategic approach in releasing varying water ratios from outlets situated at different levels. Specifically, the proposal emphasizes the controlled release of both smaller and larger volumes of water from outlets positioned at lower and higher levels within the dam structure, respectively. This deliberate variation in water release aims to bolster the overall resilience of the operational framework, contributing to a more robust and reliable system.
    Keyword Water quality assessment
    Water quality
    Decision making
    reservoir operations
    Robust decision making (RDM)
    Artificial Intelligence
    Artificial neural network (ANN)
    Classification and Regression Tree (CART)
    Patient Rule Induction Method (PRIM)
    Copyright Holder Elsevier
    Copyright Year 2024
    Copyright type All rights reserved
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 149 Abstract Views  -  Detailed Statistics
    Created: Fri, 13 Sep 2024, 01:29:55 JST by Haideh Beigi on behalf of UNU INWEH