Robust multiobjective reservoir operation and risk decision-making model for real-time flood control coping with forecast uncertainty

Huang, Xin; Xu, Bin; Zhong, Ping‐an; Yao, Hongyi; Hao, Yue; Zhu, Feilin; Lü, Qingwen; Sun, Yu; Mo, Ran; Li, Zhen; Liu, Weifeng

doi:10.1016/j.jhydrol.2021.127334

Cited by 30 publications

(11 citation statements)

References 41 publications

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“…As a key aspect of water hazard control, accurate forecasting of river runoff is important. Under the influence of changing environment, the runoff formation process and evolution mechanism are complex and variable, and the basin steady-state assumption is no longer valid [1][2]. Currently, it is important to explore disaster forecasting models suitable for different basins to achieve proactive and scientific flood control [3].…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Liu

Li-hu

et al. 2023

IEEE Access

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To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient (Ens) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.

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Section: Introductionmentioning

confidence: 99%

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Liu

Li-hu

et al. 2023

IEEE Access

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“…In these studies, has been emphasized on changing the optimization method (Ming et al, 2015;Zarei et al, 2019), focusing on the control details of a phenomenon (such as flood) (Chen et al, 2020;Huang et al, 2022), the use of different methods in the evaluation of the uncertainty of parameters (Ahmadi et al, 2010;Dai et al, 2018;Li et al, 2021;Muronda et al, 2021;Tabari, 2015), and presenting different multi-criteria decision-making (MCDM) approaches to select the best optimal policies from the set of non-dominate solutions produced by multi-objective metaheuristic algorithms (Malekmohammadi et al, 2011;Tabari & Abyar, 2021;Yang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: A case study of Karaj AmirKabir dam

Tabari

Safari

2023

Preprint

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The process of optimal operation of multipurpose reservoirs is accompanied by large dimensions of decision variables and the uncertainty of hydrological parameters and water demands. Therefore, in determining the optimal operation policies (OOPs), the decision making for water allocation is faced with problems and complexities. One of the effective approaches for sustainable management and optimal allocation from water resources is the multi-objective structural development based on the uncertainty of input parameters. The purpose of this study is to provide OOPs from Karaj AmirKabir multi-purpose reservoir with applying uncertainty in the inflow to reservoir and downstream water demand. The proposed approach has been investigated in two certain and uncertain models, and three objective functions of the system including maximizing hydropower generation, water supply demands, and flood control have been considered to formulate OOPs. Non-dominated sorting genetic algorithm-II (NSGA-II) was performed to optimize the three proposed objective functions and by applying multi-criteria decision making (MCDM) methods, the best operation scenario was selected. In the uncertainty model, using the interval method and repeated implementation of the deterministic model for completely random scenarios that generated based on the variation interval of the uncertain parameters, the non-deterministic optimal allocation values were produced. Based on these optimal allocation values and the fitting of the standard probability distribution on it, the probability of occurrence of the deterministic allocation values was determined. Production of optimal probabilistic allocation policies can be very useful and efficient in providing real vision to managers to select appropriate policies in different conditions and rare hydrological events. The results obtained from the certain model shows that as a result of optimal allocation to demands, the fuzzy reliability, resiliency, and system stability indexes were improved to 67.81, 21.99, and 24.98 percentage, respectively. Also, in an uncertain model, applying changes of 48% and 22%, respectively for the inflow and downstream demand has led to changes of 23%, 55%, and 18%, respectively, in the first, second, and third objective functions. The highest impact from uncertain conditions, has been related to the water supply demands with 55% of the range of variations So, the water supply demands, has a higher sensitivity and priority than other reservoir objective functions under uncertain conditions. Another important result extracted from this study is to determine the monthly probability of optimal allocations achievement. Accordingly, in the warm seasons and years in which the reservoir is facing drought, the occurrence probability of the optimal allocations decreases. Given the comprehensiveness of the proposed methodology, this approach is a very suitable tool for determining the optimal water allocations as probabilistic based on the scenarios desired by managers and reservoir operators.

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“…Reservoir dams are usually used as one of the important engineering structures to meet various goals such as water supply demands of domestic, industry, agriculture, environment, flood control, hydroelectric energy production, ecological health, and shipping and they are called multi-purpose reservoirs. Considering the complexity governing the input and output components of reservoirs, diverse competitive goals, dynamic characteristics, non-linear behavior and uncertainty of parameters, the management and operation of these structures requires a realistic and intelligent approach (Al-Jawad & Tanyimboh, 2017;Bozorg-Haddad et al, 2021;Huang et al, 2022;Yang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The use of a multi-objective approach in reservoir management to minimize the possibility of flooding in the upstream and downstream areas of the dam under uncertain scenarios of inflow to the reservoir was carried out by Huang et al in 2022. In this study, which deals with one aspect of reservoir operation (i.e. flood control), using AR-MOEA multi-objective algorithm (Adaptive Reference Multiobjective Evolutionary Algorithm), has simultaneously controlled the possibility of risk, vulnerability and reversibility of flooding to support the intelligent control of the operation of a reservoir under real conditions (Huang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: a case study of Karaj AmirKabir dam

Tabari

Safari

2023

Soft Comput

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The process of optimal operation of multipurpose reservoirs is accompanied by large dimensions of decision variables and the uncertainty of hydrological parameters and water demands.Therefore, in determining the optimal operation policies (OOPs), the decision making for water allocation is faced with problems and complexities. One of the effective approaches for sustainable management and optimal allocation from water resources is the multi-objective structural development based on the uncertainty of input parameters. The purpose of this study is to provide OOPs from Karaj AmirKabir multi-purpose reservoir with applying uncertainty in the inflow to reservoir and downstream water demand. The proposed approach has been investigated in two certain and uncertain models, and three objective functions of the system including maximizing hydropower generation, water supply demands, and flood control have been considered to formulate OOPs. Non-dominated sorting genetic algorithm-II (NSGA-II) was performed to optimize the three proposed objective functions and by applying multi-criteria decision making (MCDM) methods, the best operation scenario was selected. In the uncertainty model, using the interval method and repeated implementation of the deterministic model for completely random scenarios that generated based on the variation interval of the uncertain parameters, the nondeterministic optimal allocation values were produced. Based on these optimal allocation values and the fitting of the standard probability distribution on it, the probability of occurrence of the deterministic allocation values was determined. Production of optimal probabilistic allocation policies can be very useful and efficient in providing real vision to managers to select appropriate policies in different conditions and rare hydrological events. The results obtained from the certain model shows that as a result of optimal allocation to demands, the fuzzy reliability, resiliency, and system stability indexes were improved to 67.81, 21.99, and 24.98 percentage, respectively. Also, in an uncertain model, applying changes of 48% and 22%, respectively for the inflow and downstream demand has led to changes of 23%, 55%, and 18%, respectively, in the first, second, and third objective functions. The highest impact from uncertain conditions, has been related to the water supply demands with 55% of the range of variations So, the water supply demands, has a higher sensitivity and priority than other reservoir objective functions under uncertain conditions.Another important result extracted from this study is to determine the monthly probability of optimal allocations achievement. Accordingly, in the warm seasons and years in which the reservoir is facing drought, the occurrence probability of the optimal allocations decreases. Given the comprehensiveness of the proposed methodology, this approach is a very suitable tool for determining the optimal water allocations as probabilistic based on the scenarios desired by managers and reservoir operators.

show abstract

Robust multiobjective reservoir operation and risk decision-making model for real-time flood control coping with forecast uncertainty

Cited by 30 publications

References 41 publications

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: A case study of Karaj AmirKabir dam

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: a case study of Karaj AmirKabir dam

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