Reservoir Flood Control Operation Research Articles

A multi-criteria decision-making model dealing with correlation among criteria for reservoir flood control operation

Flood control operation in a multi-reservoir system is a multi-criteria decision-making (MCDM) problem, in which the considered criteria are often correlated with each other. In this paper, we propose an MCDM model for reservoir flood control operation to deal with correlation among criteria. Considering the flood control safety of reservoirs and downstream protected regions, we establish the hierarchical structure of the criterion system. We use the principal component analysis method to eliminate the correlation, and transform the original criterion system into an independent comprehensive criterion system. The comprehensive decision matrix coupled with the weight vector obtained by the improved entropy weight method serves as the input to TOPSIS method, fuzzy optimum method, and fuzzy matter-element method, by which we determine the ranking order of the alternatives. We apply the proposed model to a cascade system of reservoirs at the Daduhe River basin in China. The results show that the dimensionality of the criterion system is reduced and the correlation among criteria is eliminated simultaneously, and the ranking order of the alternatives is reasonable. The proposed model provides an effective way to deal with correlation among criteria, and can be extended to wider applications in many other MCDM problems.

Credibility theory based dynamic control bound optimization for reservoir flood limited water level

Summary The dynamic control operation of reservoir flood limited water level (FLWL) can solve the contradictions between reservoir flood control and beneficial operation well, and it is an important measure to make sure the security of flood control and realize the flood utilization. The dynamic control bound of FLWL is a fundamental key element for implementing reservoir dynamic control operation. In order to optimize the dynamic control bound of FLWL by considering flood forecasting error, this paper took the forecasting error as a fuzzy variable, and described it with the emerging credibility theory in recent years. By combining the flood forecasting error quantitative model, a credibility-based fuzzy chance constrained model used to optimize the dynamic control bound was proposed in this paper, and fuzzy simulation technology was used to solve the model. The FENGTAN reservoir in China was selected as a case study, and the results show that, compared with the original operation water level, the initial operation water level (IOWL) of FENGTAN reservoir can be raised 4 m, 2 m and 5.5 m respectively in the three division stages of flood season, and without increasing flood control risk. In addition, the rationality and feasibility of the proposed forecasting error quantitative model and credibility-based dynamic control bound optimization model are verified by the calculation results of extreme risk theory.

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RFCO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RFCO (MO-RFCO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO–EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RFCO problem. MO-PSO–EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RFCO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO–EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO–EDA is suitable for solving MO-RFCO problems.

Stage-wise optimizing operating rules for flood control in a multi-purpose reservoir

Summary This paper presents a generic framework of release rules for reservoir flood control operation during three stages. In the stage prior to flood arrival, the rules indicate the timing and release discharge of pre-releasing reservoir storage to the initial level of flood control operation. In the stage preceding the flood peak, the rules prescribe the portion of inflow to be detained to mitigate downstream flooding, without allowing the water surface level of reservoir to exceed the acceptable safety level of surcharge. After the flood peak, the rules suggest the timing for stepwise reduction of the release flows and closing the gates of spillways and other outlets to achieve the normal level of conservation use. A simulation model is developed and linked with BOBYQA, an efficient optimization algorithm, to determine the optimal rule parameters in a stage-wise manner. The release rules of Shihmen Reservoir of Taiwan are established using inflow records of 59 historical typhoons and the probable maximum flood. The deviations from target levels at the end of different stages of all calibration events are minimized by the proposed method to improve the reliability of flood control operation. The optimized rules satisfy operational objectives including dam safety, flood mitigation, achieving sufficient end-of-operation storage for conservation purposes and smooth operation.

Multi-objective Immune Algorithm with Preference-Based Selection for Reservoir Flood Control Operation

In reservoir flood control operation, the safety of upstream and downstream of the dam are the main two optimization goals with conflicts. In addition, the irrigation water demands is also an important issue considered by decision makers. Therefore, the dispatching schemes that meet the final water level constraint are preferred. Considering such preference in decision making, a novel preference-based selection operator is developed and combined with immune inspired optimization technique to form the proposed multi-objective immune algorithm with preference- based selection (MOIA-PS) for reservoir flood control operation. The unique of MOIA-PS is that it intends to obtain a set of preferred Pareto optimal solutions that located within a part of preferred area on the Pareto front rather than to find a good approximation of the entire Pareto front as most existing methods did. Experimental results on four typical floods at the Ankang reservoir have indicated that the preferred non-dominated solutions are distributed within a local area of preferred PF region. And the newly designed preference-based selection operator can guide the search of MOIA-PS towards the preferred PF region. Comparing with the outstanding multi-objective evolutionary algorithm NSGAII and the immune inspired multi-objective optimization algorithm NNIA, the proposed MOIA-PS obtains more non-dominated solutions that densely and evenly scattered within the preferred area of the Pareto front. MOIA-PS can find finding dispatching schemes that not only reduce the flood peak significantly and guarantee the dam safety well but also satisfy the irrigation water demands. It is a more efficient use of the computing efforts.

Reservoir flood control operation based on chaotic particle swarm optimization algorithm

Reservoir flood control operation is a complex engineering optimization problem with a large number of constraints. In order to solve this problem, a chaotic particle swarm optimization (CPSO) algorithm based on the improved logistic map is presented, which uses the discharge flow process as the decision variables combined with the death penalty function. According to the principle of maximum eliminating flood peak, a novel flood control operation model has been established with the goal of minimum standard deviation of the discharge flow process. At the same time, a piecewise linear interpolation function (PLIF) is applied to deal with the constraints for solving objective function. The performance of the proposed model and method is evaluated on two typical floods of Three Gorges reservoir. In comparison with existing models and other algorithms, the proposed model and algorithm can generate better solutions with the minimal flood peak discharge and the maximal peak-clipping rate for reservoir flood control operation.

Optimal Hedging Rules for Reservoir Flood Operation from Forecast Uncertainties

This paper develops optimal hedging rules for reservoir flood control operation under hydrological uncertainty using hydroeconomic and mathematical analysis. The capacity to convey flood flows is sometimes a scarce resource. Hedging for flood operations uses reservoir storage to allocate the expected flood-safety margin (EFSM, i.e., the gap between expected flood volume and flood-conveyance capacity) optimally between present and future periods. Optimal flood-operation hedging falls into three cases, namely, (1) for large expected floods, all flood storage and almost all channel-conveyance capacity are used in the current period to cope with the current, more certain, and urgent flood risk; (2) for medium expected floods, the available EFSM is balanced between the current and future periods, but a larger portion of the total EFSM remains allocated to the current stage; and (3) for small expected floods, the future stage receives greater EFSM allocation by keeping reservoir space empty in the current period. Optimal hedging for flood operation is illustrated by a curve similar to that of hedging for water supply. The physical implications of hedging highlight the economic significance of this practice for balancing the marginal value of scarce flood-management resources under uncertainty.

An extended compromise ratio model with an application to reservoir flood control operation under an interval-valued intuitionistic fuzzy environment

This paper presents a novel multiple attribute group decision-making (MAGDM) model based on the compromise ratio method under an interval-valued intuitionistic fuzzy (IVIF) environment. The compromise ratio method under uncertainty is introduced by a group of experts based on the concept that the chosen alternative should be as close as possible to the IVIF-positive-ideal point and as far away from the IVIF-negative-ideal point as possible concurrently. First, an IVIF-weighted geometric averaging (IVIFWGA) operator is employed to aggregate all individual IVIF-decision matrices provided by a group of experts into a collective IVIF-decision matrix. Two new basic IVIF-operations are introduced to handle the evaluation process. Then, an extended collective index in an IVIF environment is proposed to discriminate among alternatives for the evaluation process in terms of subjective and objective information. Finally, to demonstrate the suitability and applicability of the proposed IVIF-MAGDM model, an application example of reservoir flood control operation is given from the recent literature.

A novel multi-objective electromagnetism-like mechanism algorithm with applications in reservoir flood control operation

Reservoir flood control operation (RFCO) is a complex problem that involves various constraints and purposes, which include the safety of the dam, watershed flood control and navigation. These objectives often conflict with each other. Thus, traditional methods have difficulty in solving the multi-objective problem efficiently. In this paper, a multi-objective self-adaptive electromagnetism-like mechanism (MOSEM) algorithm is introduced in the local searching operation of the proposed method. To enhance the optimization ability of EM, a self-adaptive parameter is applied in the local search operation of MOSEM for adjusting the values of parameters dynamically. Moreover, MOSEM is tested by several benchmark test problems and compared with some well-known multi-objective evolutionary algorithms. A case study is also used for solving RFCO problems of the Three Georges Reservoir by using the multi-objective cultured differential evolution (MOCDE), non-dominated sorting genetic algorithm-II (NSGA-II) and proposed MOSEM methods. The study results reveal that MOSEM can provide alternative Pareto-optimal solutions (POS) with better convergence properties and diversification.

Multistep‐ahead flood forecasts by neuro‐fuzzy networks with effective rainfall–run‐off patterns

AbstractThe purpose of this study is to construct a multistep‐ahead flood forecasting model based on the precise information of the rainfall‐runoff process in a watershed during typhoon events through neuro‐fuzzy networks. To achieve this goal, the nonparametric Kendall trend test was implemented for identifying appropriate rainfall lag times, and then the multistep‐ahead flood forecasting was carried out by the adaptive neuro‐fuzzy inference system (ANFIS)‐based hydrological models with different input combinations. Hydrological data collected during 13 typhoon events in the Shihmen Reservoir watershed of Taiwan were used to train and validate the forecasting models. Results reveal that rainfall and inflow had similar patterns with a time shift of 5 up to 7 h, and the ANFIS‐based model with inputs that involved effective time‐delayed rainfall identified by the Kendall trend test performed better than the other comparative models. Results demonstrate that accurate inflow forecasts can be achieved up to a lead time of 5 h, which is very valuable information on real‐time reservoir operation for flood control.

Estimation of reservoir flood control operation risks with considering inflow forecasting errors

A method for quantifying inflow forecasting errors and their impact on reservoir flood control operations is proposed. This approach requires the identification of the probability distributions and uncertainty transfer scheme for the inflow forecasting errors. Accordingly, the probability distributions of the errors are inferred through deducing the relationship between its standard deviation and the forecasting accuracy quantified by the Nash–Sutcliffe efficiency coefficient. The traditional deterministic flood routing process is treated as a diffusion stochastic process. The diffusion coefficient is related to the forecasting accuracy, through which the forecasting errors are indirectly related to the sources of reservoir operation risks. The associated risks are derived by solving the stochastic differential equation of reservoir flood routing via the forward Euler method. The Geheyan reservoir in China is selected as a case study. The hydrological forecasting model for this basin is established and verified. The flood control operation risks in the forecast-based pre-release operation mode for different forecasting accuracies are estimated by the proposed approach. Application results show that the proposed method can provide a useful tool for reservoir operation risk estimation and management.

Multi-Objective Decision Model of Reservoir Flood Control Operation

To reservoir flood control operation of the multi-objective decision making. The proposed reservoir flood control operation. Then, according to the model, the weight of goals with realistic and easy to program have been obtained by iterative calculation. According to the weight goal has been to meet the goal of accuracy the value of integrated decision-making and fuzzy partition, and according to the results determined the type of the decision-making and an order. The example shows that the multi-objective decision making model may use in the reservoir the flood prevention dispatch practice.

Scheme optimum selection for dynamic control of reservoir limited water level

Dynamic control of reservoir limited water level is important to reservoir flood control operation. A reasonable limited water level can best utilize flood water resources in addition to flood control. This paper is a trial application of the fuzzy information entropy matter-element evaluation method (FIEMEM) as an optimal selection of dynamic control of limited water level. In this method, compound matter elements are established first, followed by establishment of an evaluation model and choice of the optimal scheme on the basis of fuzzy information entropy. In determining weights, a combined weighting method in game theory is adopted to combine experiential weights and mathematical weights so as to eliminate one-sidedness of the single weighting method. Finally, the feasibility of this optimization method is verified by citing dynamic control of Biliuhe reservoir limited water level as an example.

Risk analysis of flood control operation mode with forecast information based on a combination of risk sources

Risk analysis of reservoir flood control operation mode with forecast information (FCOMFI) is an important basis for the design and implementation of FCOMFI. Most of current researches on this issue are incomplete as they only consider flood forecast errors, but not many other uncertainties in reservoir routing. In order to obtain an integrated risk rate of FCOMFI, this paper analyzes four uncertainties, i.e. hydrological, hydraulic, stage-storage uncertainty and time-delay uncertainty, as well as their probability distributions. On the basis of this analysis, an integrated risk analysis model of FCOMFI for reservoirs and its lower reach is established involving the above-mentioned four uncertainties, and this model is solved by Monte Carlo simulation based on Latin hypercube sampling. The simulation results, with Baiguishan reservoir as the example, show that the integrated risk rates of FCOMFI are less than those of the flood control operation mode without forecast information. This article presents the highest limited water level that satisfies flood control safety requirements of the lower reach.

Reservoir flood control operation model incorporating multiple uncontrolled water flows

This study presents a weighted pre-emptive goal programming model formulation for coordinated reservoir operation, with easy inclusion of uncontrolled water flows. The model is combined with a multiple water inflows forecasting model, and can be used for real time reservoir operation. Water flow routing from various upstream sites is accounted by with a single compact equation. Integration of controlled and uncontrolled water flows in the optimization model simplifies the operation model, resulting in accurate computation of the downstream water flow. Multiple objectives with water storage and flow variables are used to derive optimal regulation for a reservoir system under flood conditions. For real time operations, the model can be used to determine optimal water release rates for a current period, on the basis of an optimal water release schedule for an operating horizon (T). The model is applied to the flood control operation of reservoirs in the Narmada River Basin (India), with three controlled and three uncontrolled water flows affecting the downstream flow at Hoshangabad. Reservoir water storage and downstream control point flows are zoned, with prioritized objectives used to derive the optimal water release rates. Model applications to the 1999 flood event in the Narmada River Basin with observed and forecasted inflows illustrates that, if water inflows were known through a forecasting technique well in advance, the coordinated operation of the reservoirs could substantially reduce the peak water flows at the control points. The study also indicates that uncontrolled channel flows at the damage site were sufficiently high to cause flooding at the damage site.

Novel Multiobjective Shuffled Frog Leaping Algorithm with Application to Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a large scale multiobjective problem with complex constraints that require powerful algorithms to solve it. As a new metaheuristic evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the potential ability to solve multiobjective optimization problems because of its group evolution characteristic. In this paper, we present a novel multiobjective shuffled frog leaping algorithm (MOSFLA), which incorporates an archiving strategy based on self-adaptive niche method to maintain the nondominated solutions, and improves the memetic evolution process of SFLA to adapt to the multiobjective optimization problem. The numerical experiments of five Zitzler-Deb-Thiele functions indicate that MOSFLA yields better-spread solutions and converges closer to the true Pareto frontier than non-denominated sorting genetic algorithm (NGSA)-II and SPEA2. Furthermore, MOSFLA is applied to solve RFCO of the Three Gorges Project, and the results demonstrate that this algorithm can generate a solution set with uniform spread and good convergence for the problems with two conflicting objectives, including minimizing the highest reservoir water level and minimizing the peak flood discharge. Additionally, if compared with dynamic programming and NGSA-II, MOSFLA is verified to be more efficient and competitive, and thus can be provided as a new effective alternative for solving the complex reservoir operation problems.

Multi-objective Cultured Differential Evolution for Generating Optimal Trade-offs in Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a complex problem because it needs to consider multiple objectives and a large number of constraints. Traditional methods usually convert multiple objectives into a single objective to solve, using weighted methods or constrained methods. In this paper, a new approach named multi-objective cultured differential evolution (MOCDE) is proposed to deal with RFCO. MOCDE takes cultural algorithm as its framework and adopts differential evolution (DE) in its population space. Considering the features of DE and multi-objective optimization, three knowledge structures are defined in belief space to improve the searching efficiency of MOCDE. MOCDE is first tested on several benchmark problems and compared with some well known multi-objective optimization algorithms. On achieving satisfactory performance for test problems, MOCDE is applied to a case study of RFCO. It is found that MOCDE provides decision makers many alternative non-dominated schemes with uniform coverage and convergence to true Pareto optimal solutions in a short time. The results obtained show that MOCDE can be a viable alternative for generating optimal trade-offs in reservoir multi-objective flood control operation.

Real-Time Reservoir Operation for Flood Control Using Artificial Intelligent Techniques

Article Real-Time Reservoir Operation for Flood Control Using Artificial Intelligent Techniques was published on November 1, 2010 in the journal International Journal of Nonlinear Sciences and Numerical Simulation (volume 11, issue 11).

Optimal Reservoir Operation for Flood Control Using Folded Dynamic Programming

Folded Dynamic Programming (FDP) is adopted for developing optimal reservoir operation policies for flood control. It is applied to a case study of Hirakud Reservoir in Mahanadi basin, India with the objective of deriving optimal policy for flood control. The river flows down to Naraj, the head of delta where a major city is located and finally joins the Bay of Bengal. As Hirakud reservoir is on the upstream side of delta area in the basin, it plays an important role in alleviating the severity of the flood for this area. Data of 68 floods such as peaks of inflow hydrograph, peak of outflow from reservoir during each flood, peak of flow hydrograph at Naraj and d/s catchment contribution are utilized. The combinations of 51, 54, 57 thousand cumecs as peak inflow into reservoir and 25.5, 20, 14 thousand cumecs respectively as peak d/s catchment contribution form the critical combinations for flood situation. It is observed that the combination of 57 thousand cumecs of inflow into reservoir and 14 thousand cumecs for d/s catchment contribution is the most critical among the critical combinations of flow series. The method proposed can be extended to similar situations for deriving reservoir operating policies for flood control.

A fuzzy optimization method for multicriteria decision making: An application to reservoir flood control operation

This paper presents a fuzzy optimization method based on the concept of ideal and anti-ideal points to solve multi-criteria decision making problems under fuzzy environments. The quantitative criteria values of each alternative are represented by triangular fuzzy numbers, and its qualitative counterparts and the weight of each criterion are described by linguistic terms, which can also be expressed as triangular fuzzy numbers in the proposed method. With the definition of fuzzy ideal and anti-ideal weight distances, an objective function is constructed to derive the optimal evaluation for each alternative denoted by a fuzzy membership degree. The ranking of alternatives and the best one can be determined directly on the basis of the fuzzy membership degrees without a need to compare fuzzy numbers. The evaluation process is simple and easy to use in practice. A case study of reservoir flood control operation is given to demonstrate the proposed method’s effectiveness.