Investigation of a composite two-phase hedging rule policy for a multi reservoir system using streamflow forecast

Mostaghimzadeh, Ehsan; Adib, Arash; Ashrafi, Seyed Mohammad; Kişi, Özgür

doi:10.1016/j.agwat.2022.107542

Cited by 13 publications

(2 citation statements)

References 39 publications

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“…The studies demonstrate improvements in water deficit, system vulnerability, and operation performance compared to conventional rule curves and standard operation policies [59]. The models incorporate forecasted inflows using feature selection methods, ensemble learning techniques, and optimization algorithms to determine effective forecast horizons [61,62]. The research emphasizes the need to consider nonstationary time series, future conditions, and climate-atmospheric indices in reservoir management and decision-making processes [63,64].…”

Section: Figurementioning

confidence: 99%

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Sriworamas,

Prasanchum,

Ashrafi

et al. 2023

Water

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This paper presents a comprehensive review of optimization algorithms utilized in reservoir simulation-optimization models, specifically focusing on determining optimal rule curves. The study explores critical conditions essential for the optimization process, including inflow data, objective and smoothing functions, downstream water demand, initial reservoir characteristics, evaluation scenarios, and stop criteria. By examining these factors, the paper provides valuable insights into the effective application of optimization algorithms in reservoir operations. Furthermore, the paper discusses the application of popular optimization algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and tabu search (TS), highlighting how researchers can utilize them in their studies. The findings of this review indicate that identifying optimal conditions and considering future scenarios contribute to the derivation of optimal rule curves for anticipated situations. The implementation of these curves can significantly enhance reservoir management practices and facilitate the resolution of water resource challenges, such as floods and droughts.

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Section: Figurementioning

confidence: 99%

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Sriworamas,

Prasanchum,

Ashrafi

et al. 2023

Water

Self Cite

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“…Timely and precise data on river streamflow holds particular significance, notably aiding in the regulation of reservoir outflows during periods of low streamflow and offering advance alerts for impending or potential flood events. From an economic standpoint, accurate forecasting of river streamflow serves as a pivotal factor in optimizing hydropower generation and meeting agricultural water demands 3 . In addition, providing decision-makers with precise river-streamflow forecasting information could aid in the accurate assessment of flood magnitude and prevent casualties and limitless damage to property and infrastructure 4 .…”

Section: Introductionmentioning

confidence: 99%

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Van Thieu,

Nguyen,

Sherif

et al. 2024

Sci Rep

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Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge–Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson’s correlation index (R) of 0.9374, and a Nash–Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

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Effectiveness of Adaptive Operating Rules for Reservoirs

Garrote

Granados

Spiliotis

et al. 2023

Water Resour Manage

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In this paper, a methodological proposal is made to develop an adaptive decision support system for reservoir management. The system is based on an optimization model that determines operating rules that meet certain optimality conditions based on the state of the reservoir at a certain time and on a streamflow forecast, if available. The model is based on Spiliotis et al. in Water Resour Manage 30:5759-5778, where a general methodology was developed to specify static operating rules for reservoir systems. The proposed methodology consists of modifying the optimization procedure developed in the previous work to dynamically update operating rules to adapt management to the changing situation. To evaluate its effectiveness, the methodology was applied to the Pisuerga-Carrión reservoir system, in the Spanish part of the Duero basin. The results obtained with adaptive rules were compared with those obtained applying the same static operating rule for the entire analysis period. Adaptive rules were found to lead to better operating results, particularly if a successful streamflow forecast method is available.

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Investigation of a composite two-phase hedging rule policy for a multi reservoir system using streamflow forecast

Cited by 13 publications

References 39 publications

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Effectiveness of Adaptive Operating Rules for Reservoirs

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