Reservoir Flood Control Operation Research Articles

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

AbstractInforming reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

Study on the constraint handling method for high-dimensional optimization of cascade reservoirs

Multicomplex constraints often need to be considered in the optimal operation of cascade reservoirs with high-dimensional decision variables, making it difficult for traditional optimization methods and modern intelligent algorithms to solve such problems. Therefore, this study proposes a constraint handling method combining a penalty function nested DPSA-POA and an intelligent algorithm, to determine the optimal flood control operation of cascade reservoirs in the middle reaches of the Ganjiang River, a problem with decision variables of up to 2196 dimensions. The results indicate that the constraint handling method proposed in this paper can solve high-dimensional optimization problems in three modes: continuous nesting (Mode 1), optimization with intelligence after obtaining a feasible solution (Mode 2), and optimization with the DPSA-POA after obtaining a feasible solution (Mode 3). Of the three modes, Mode 2 has the highest accuracy, but its calculation time is approximately 10 h. Although the accuracy of Mode 3 is slightly worse, can only achieve 98%–99% of Mode 2, its calculation time is only approximately 1–3 h. The comprehensive performance of Mode 1 is poor, the convergence accuracy can only reach 97% of Mode 2, which corresponds to a calculation time of approximately 4–6 h. The existing superiority of feasibility (SF), stochastic ranking (SR), penalty function (PF), ε-constraint (EC) and adaptive ε-constraint (Adaptive EC) methods cannot converge to a feasible solution stably, and the accuracy of the results of these methods under the condition of obtaining a feasible solution is significantly lower than that of Mode 2 and Mode 3, reaching only 95%.

Analysis of seepage failure probability for high core rockfill dams during rapid drawdown of reservoir water level

Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions.

Research on Flood Risk Control Methods and Reservoir Flood Control Operation Oriented towards Floodwater Utilization

Since improving floodwater utilization may increase flood risk, flood risk control methods for trade-offs between these factors have research value. This study presented a flood risk control method oriented towards floodwater utilization which considers multiple main flood risk factors. The proposed method not only achieves the boundaries of the flood limited water level (FLWL) under various acceptable risks but also dynamically controls the water level to enhance floodwater utilization. A case study conducted on the Danjiangkou reservoir yielded the following results: (1) The proposed method provides FLWL dynamic control boundaries under various acceptable risks. (2) The proposed method reveals the potential to raise the FLWL, with a possibility to raise it by 1.00 m above the present FLWL under the absence of flood risk. (3) The available flood resources in both the wet and dry seasons increase, on average, by 0.83 and 0.81 billion m3, and the flood risk remains within the acceptable range after raising the FLWL by 1.00 m, which contributes to enhancing floodwater utilization.

Stable Improved Dynamic Programming Method: An Efficient and Accurate Method for Optimization of Reservoir Flood Control Operation

Stable Improved Dynamic Programming Method: An Efficient and Accurate Method for Optimization of Reservoir Flood Control Operation

Stochastic multi-criteria decision making framework based on SMAA-VIKOR for reservoir flood control operation

ABSTRACT In reservoir flood control operation, candidate reservoir operation alternatives are evaluated, ranked and selected by a multi-criteria decision making (MCDM) method. However, deterministic methods cannot handle MCDM problems where uncertainties exist in both criteria performance values (PVs) and criteria weights (CWs). To solve the issue, a SMAA-VIKOR model integrating stochastic multicriteria acceptability analysis (SMAA) theory with the Viekriterijumsko Kompromisno Rangiranje (VIKOR) method is established based on Aydogan and Ozmen (2017). Moreover, we conduct a comprehensive evaluation of the model’s decision making results, including risk assessment and sensitivity analysis. The case study applied to a flood control system in Dadu River in China indicates that the SMAA-VIKOR model can quickly identify the optimal alternative that meets the subjective preferences of decision makers, and has remarkable advantages compared to the traditional SMAA-2 model and the deterministic VIKOR method, which can provide theoretical support for making a robust reservoir release decision.

Multi-objective robust optimization of reservoir operation for real-time flood control under forecasting uncertainty

Flood control operation is one of the effective measures to reduce flood risks. Since flood forecasting plays a critical role in real-time reservoir flood control operation, it is necessary to involve forecasting uncertainty in the optimal reservoir operation to inform decision-making. It has always been a difficult task to reduce flood risks by adjusting flood control strategies. To tackle this challenge, this study developed a multi-objective robust optimization methodology for real-time reservoir flood control operation, which mainly coped with forecast uncertainty. Three machine learning (ML) models, including back propagation neural network (BP), long short-term memory neural network (LSTM) and extreme learning machine (ELM) were adopted to forecast the short-term reservoir inflow. A stacking ensemble multi-ML model (SEM) was then applied to integrate the forecasting results of the above models. Furthermore, a multi-objective robust optimal operation model (MOROU) integrating both upstream and downstream safety was established to reduce flood risks and evaluate the impact of forecasting uncertainty on the reservoir operation. To maximize the efficiency of reservoir utilization, this study defined a new indicator of reservoir reserved capacity adaptation (RRCA) as one of the optimization indicators. To better solve the complex multi-objective problem, a scenario to point (STP) method was proposed for searching robust solutions of multi-objective optimization models. Methodologies were validated through an application to the Lishimen reservoir, China. Three main conclusions were derived from the study: (1) Three ML models performed well in flood forecasting with BP being slightly better than the other two, and the SEM method was found to be able to incorporate the characteristics of each model, outperforming the individual models; (2) MOROU showed a narrower distribution of flood risk both upstream and downstream and achieved an approximately 1.5% reduction in the maximum value of the highest water level; (3) RRCA was verified to enable to reduce the discharge flow by an average of 4.52% without occupying additional flood control reservoir capacity, confirming that it can be used as an optimization indicator to improve the utilization of the reservoir. The proposed method can provide robust strategies for real-time reservoir flood control under forecast uncertainty.

An inverse-problem approach to detect outliers in rainfall measurements of ground gauges for robust reservoir flood control operation

This study proposes an approach to detect and remove systematic outliers in rainfall measurement of ground gauges in the watershed of a reservoir. This method utilizes a conceptual rainfall-runoff model with the downstream observed reservoir inflow to supervise the detection and cleanse of upstream rain measurements. The analysis is based on a nonlinear optimization with the objective designed as only removing outliers which leads to relevant improvement in runoff simulation to satisfy required precision. The minimum acceptable precision in hydrological simulation is specified based on the objective of reservoir flood control operation. This method can be utilized to purify historical rain data for hydrological studies, as well as to estimate the current floodwater detained in the watershed for more robust flood management in real-time. A realistic case study along with synthetically designed experiments are demonstrated to verify the effectiveness of the proposed methods.

Multi-Reservoir Flood Control Operation Using Improved Bald Eagle Search Algorithm with ε Constraint Method

The reservoir flood control operation problem has the characteristics of multiconstraint, high-dimension, nonlinearity, and being difficult to solve. In order to better solve this problem, this paper proposes an improved bald eagle search algorithm (CABES) coupled with ε-constraint method (ε-CABES). In order to test the performance of the CABES algorithm, a typical test function is used to simulate and verify CABES. The results are compared with the bald eagle algorithm and particle swarm optimization algorithm to verify its superiority. In order to further test the rationality and effectiveness of the CABES method, two single reservoirs and a multi-reservoir system are selected for flood control operation, and the ε constraint method and the penalty function method (CF-CABES) are compared, respectively. Results show that peak clipping rates of ε-CABES and CF-CABES are both 60.28% for Shafan Reservoir and 52.03% for Dahuofang Reservoir, respectively. When solving the multi-reservoir joint flood control operation system, only ε-CABES flood control operation is successful, and the peak clipping rate is 51.76%. Therefore, in the single-reservoir flood control operation, the penalty function method and the ε constraint method have similar effects. However, in multi-reservoir operation, the ε constraint method is better than the penalty function method. In summary, the ε-CABES algorithm is more reliable and effective, which provides a new method for solving the joint flood control scheduling problem of large reservoirs.

Construction of multi-objective reservoir flood control operation preference model

Construction of multi-objective reservoir flood control operation preference model

Application Research of CFD-MOEA/D Optimization Algorithm in Large-Scale Reservoir Flood Control Scheduling

Reservoir flood control has an important impact on flood protection and plays an important role in reducing the loss of people’s lives and property. In order to play an important role in flood control operation of large-scale reservoirs, a control flood dispatching multi-objective evolutionary algorithm based on decomposition (CFD-MOEA/D) is proposed. The same type of multi-objective optimization algorithm (non-dominated sorting genetic algorithm II (NSGA-II)) is introduced, and CFD-MOEA/D, NSGA-II, and traditional MOEA/D algorithms are compared. The research results show that the CFD-MOEA/D algorithm can obtain the non-dominated solution of the higher water level in the upstream, and the solution obtained by the CFD-MOEA/D algorithm is more sufficient than the NSGA-II algorithm and the MOEA/D algorithm. When analyzing the HV value curve, the uniformity and convergence of the optimal solution obtained by the CFD-MOEA/D algorithm are better than those of the other two algorithms. The optimal dispatching scheme of the CFD-MOEA/D algorithm is compared with the actual dispatching scheme of the reservoir, and it is found that the maximum upstream water level and the final water level obtained by the CFD-MOEA/D algorithm are both kept at approximately 325 m, which is consistent with the actual dispatching scheme. The new feature of the algorithm is that it uses a decomposition method from coarse to fine and improves the hourly scheduling scheme to obtain higher scheduling efficiency.

Exploring a multi-objective cluster-decomposition framework for optimizing flood control operation rules of cascade reservoirs in a river basin

Multi-objective flood control operation of cascade reservoirs is a vital issue in river basin management. However, traditional multi-objective approaches commonly provide one operation scheme only and fail to offer decision-makers with more Pareto-front options. This study explores a multi-objective cluster-decomposition framework for optimizing the flood control operation rules of cascade reservoirs in a river basin. The proposed framework involves a multi-objective optimization module, a cluster-decomposition module, and an evaluation and sorting module. The multi-objective cluster-decomposition framework simultaneously deals with three objectives: to minimize the flood peaks of flood control points (O1); to minimize the reservoir capacity used for flood control (O2); and to minimize the flood diversion volume of the flood detention area (O3). The complex flood control system composed of two cascade reservoirs, four navigation-power junctions, one flood detection area, and three flood control points in the Ganjiang River basin of China constitutes the case study. The results demonstrate that the proposed framework can significantly improve the comprehensive benefits of the cascade reservoirs, where the maximum reduction in objectives O1–O3 is 2071 m3/s (the improvement rate is 2.64 %), 219 million m3 (the improvement rate is 44.60 %), and 167 million m3 (the improvement rate is 78.13 %), respectively. Furthermore, in contrast to the traditional multi-attribute evaluation method, the proposed framework can effectively identify compromised decisions through a cluster-decomposition module, which provides beneficial trade-off guidance in making a sound decision upon Pareto-front options.

An MILP based optimization model for reservoir flood control operation considering spillway gate scheduling

Reservoir flood control operation is an important and effective measure for avoiding or reducing flood disasters. Considering that reservoir operation is a control process that essentially manages the spillway gates to increase or decrease the amount of released water, this paper focuses on studying the reservoir flood control operation which considers spillway gate scheduling. Minimizing the peak outflow from the reservoir is adopted as the optimization objective to protect downstream areas from flood disasters. In addition to the conventional reservoir constraints, the operation constraints of individual spillway gates and the influence of the reservoir forebay water level and opening degree on the gates’ discharge capacity are also well considered to obtain precise scheduling. The nonlinear factors, especially the objective function in the form of maximin, the three-dimensional water discharge curves, and the rules for the opening and closing sequence of the spillway gates, are linearized through several strategies. The original nonlinear and non-convex model is then converted into a standard mixed-integer linear programming (MILP) formulation. The results for the real-world case study indicate that: 1) the proposed model is believed to be computationally efficient with a calculation time of about 30 min; 2) for the flood events with return periods of 10 years and 100 years, the flood peaks are reduced from 4160 m3/s to 3681 m3/s and from 11007 m3/s to 9293 m3/s, representing decreases of 11.5 % and 15.6 %, respectively. The outflow curves become more stable, demonstrating that the flood control pressure on the downstream had been relieved after optimization; 3) the proposed model can produce more realistic and executable flood control operation scheduling compared with the conventional optimization model without considering spillway gate scheduling. These research findings can provide an effective reference for both the operators of reservoirs and researchers in the field of reservoir flood control operation.

A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation

A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation

Optimization of Reservoir Flood Control Operation Based on Multialgorithm Deep Learning

With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatching. Large-scale joint operation of river basins usually needs to consider meteorological and hydrological conditions, historical flood data, multireservoir engineering conditions, and multiple flood control targets, which is a complex decision-making problem. Therefore, electing the optimal operation model of reservoir flood control optimization is very important. In this paper, Luanhe River Basin is taken as the research area, and three kinds of constraints, namely, water balance constraint, reservoir flood control capacity constraint, and water release decision constraint, are set to construct the flood control optimization model. Taking the minimum square of the sum of reservoir discharge and interval flood discharge as the objective function, genetic algorithm (GA), particle swarm optimization (PSO), Spider swarm optimization (SSO), and grey wolf optimization (GWO) are introduced into flood control optimal operation to seek the minimum value of objective function, and the results are compared and analyzed. Through the analysis of optimization results, the optimization ability and convergence effect of grey wolf optimization algorithm are better than those of genetic algorithm and particle algorithm, and the results are more stable than those of spider swarm algorithm. It has a good model structure and can make full use of the results of three wolf groups for optimization. Through the analysis of scheduling results, the results of genetic algorithm and particle swarm optimization algorithm are similar, while those of spider swarm optimization algorithm and grey wolf optimization algorithm are similar and slightly better than those of the first two. Moreover, the search range of grey wolf optimization algorithm for solving long sequence problems is wider and the calculation time is shorter. Therefore, the grey wolf optimization algorithm can be applied to solve the flood control operation optimization model of Panjiakou Reservoir Group.

Research and Application of Reservoir Flood Control Optimal Operation Based on Improved Genetic Algorithm

This paper took the Foziling Reservoir in the Pi River Basin as an example, used an improved genetic algorithm to optimize the flood control dispatching during the flood process, and compared the results with the traditional genetic algorithm and the dispatching plan in the 2020 large-scale reservoir flood control operation plan. The results showed that, compared with the traditional genetic algorithm, the improved genetic algorithm saved the time for the model to determine the penalty coefficients and made the model application more convenient. At the same time, the design of the original scheduling scheme also has certain limitations. The scheduling results obtained by improving the genetic algorithm could occupy a small flood control capacity as much as possible under the premise of ensuring the safety of the reservoir itself and the downstream area.

Multi-objective risk analysis for flood control operation of a complex reservoir system under multiple time-space correlated uncertainties

Many uncertainties are involved in flood control operation of a multi-reservoir system and result in risks for the system. Risk analysis for reservoir flood control operation is essential for decision making. Traditional flood control risk analysis only takes flood forecast error as the main risk source, ignoring the influence of dynamic control of flood-limited water level on reservoir initial water level for flood regulation. In this study, the spatial correlation of reservoir initial water level errors and the spatiotemporal correlation of flood forecast errors are identified via copula function. A risk analysis model considering both upstream and downstream is established for multi-objective flood control operation of a complex reservoir system, coupling multi-dimensional uncertainties of reservoir initial water levels, flood forecast errors, reservoir capacity curve errors, reservoir discharge curve errors and river flood routing errors. The impact of multiple risk sources with spatiotemporal correlations on reservoir flood control operation is then evaluated and the competitive tradeoff between flood control risks in upstream and downstream analyzed. The model is applied to a mixed four-reservoir system in Pi River Basin in China. Results indicated that 1) there is a strong correlation between the reservoir initial water level errors in space and also between the flood forecast errors in space and time, which can be effectively described by copula function, and ignoring the correlations will underestimate the flood control risk; 2) the two objective values of upstream and downstream show a clear competitive relationship, and the solution that prefers one objective has low risk marginal benefit, thus choosing a compromise solution can balance risks; 3) coupling the initial water level uncertainty reduces the flood control risk caused by the flood forecast errors to a certain extent, but also lead to the increase of extreme risk loss.

Construction and Application of Reservoir Flood Control Operation Rules Using the Decision Tree Algorithm

Current conventional and optimal reservoir flood control operation methods insufficiently utilize historical reservoir operation data, which include rainfall, runoff generation, and inflow from the watershed, as well as the operational experience of decision makers over many years. Therefore, this study proposed and evaluated a new method for extracting reservoir flood control operation rules from historical operation data using the C4.5 algorithm. Thus, in this paper, the C4.5 algorithm is first introduced; then, the generation of the flood control operation dataset, the construction of decision tree-based (DT-based) rules, and the subsequent design of a real-time operating scheme are detailed. A case study of the Rizhao Reservoir is then employed to demonstrate the feasibility and even superiority of the operating scheme formulated using DT-based rules. Compared with previously proposed conventional and optimal reservoir operation methods, the DT-based method has the advantages of strong and convenient adaptability, enabling decision makers to effectively guide real-time reservoir operation.

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

Forecast uncertainty in real-time flood forecasting is the primary source of risk regarding reservoir flood control operation, affecting both reliability and safety of the entire system. Informing reservoir operation for real-time flood control coping with forecast uncertainty encourages overall robust control of risk probability, vulnerability, and resilience of upstream and downstream protection areas. Current stochastic programming models or chance constrained programming models (CCP) focuses on reducing either risk loss or vulnerability, thereby failing to ensure systematic control. This study established a robust multiobjective operation and risk decision-making model to inform reservoir operations. The proposed model includes three modules. First, a copula function was applied to capture multidimensional temporal dependences of forecast uncertainties, following which stochastic inflow scenarios were generated. Second, a robust multiobjective optimization (RMOO) model was established with the objectives of minimizing the probability of risk events, Conditional Value-at-Risk of risk loss (vulnerability) and the maximum sequential duration for both upstream and downstream areas. Then, an adaptive reference multiobjective evolutionary algorithm was used to solve the model and obtain noninferior solutions. Finally, the TOPSIS multicriteria decision-making method was introduced to select the compromise solution. Methodologies were verified by application to Xianghongdian reservoir, China. The main findings were as follows. (1) The copula function effectively captured the multidimensional positive temporal dependences of forecast uncertainties. (2) RMOO provided more widely distributed noninferior solutions than CCP, and performed better in lowering vulnerability of the system. (3) RMOO reduced the maximum release by 135.6 m3/s (7.33%) by increasing prerelease before the occurrence of flood peak. The proposed methodologies provide insights for supporting robust control of real-time operations of a reservoir system through simultaneous control of flood risk probability, vulnerability, and resilience.

SLOPE STABILITY ASSESSMENT OF THE UPSTREAM SLOPE OF THE DRY MOUNTAIN FLOOD CONTROL RESERVOIR DURING THE FLOOD

The flood control is one of the priority goal for successful economic activity on the areas that are periodically suffer from floods. Such areas are the mountainous regions of the Ukrainian Carpathian Mountains. Floods on the mountain rivers are repeated several times each year, and are characterized by the sudden water level rise with almost the same rapid decrease of the water level. Active flood protection measures include dry mountain flood control reservoirs, the principle of which is to transform part of the flood runoff and to accumulate water for the short time in the the artificial reservoir, with followed rapid emptying to the minimum level. The complex hydraulic regime is formed in the body of the dam which forms the flood control reservoir during the flood, that is different from the operation of the water permanent reservoir. The design of the flood control structures is car-ried out in accordance with Ukrainian building codes for the construction of the water reservoirs with constant water level, and require testing the stability of the downstream slope for the maximum water levels under steady state seepage conditions and assessment the upstream slope stability during the water level decreasing from the maximum level calculated in the steady state condition, these calculations do not correspond to the real seepage processes in the body of the dam of the dry flood control reservoir. Therefore, the purpose of this work is to determine the necessary boundary conditions of the flood control reservoir operation and upstream slope stability assessment by the limit equilibrium method. In the article the operation of the dry mountain flood control reservoir was analysed and found that the dam was characterized by two states: dry reservoir with water minimum water level and variable position of the seepage curve in the core and the upstream prism during the flood. The main factors influencing the upstream slope stability are the physical and mechanical properties of the soil, the laying of the slope, the period of time when the high-water level is maintained and the intensity of water level dropping. The upstream slope stability was evaluated by the Morgenstern & Price and Ordinary methods on the Slope/w software package. After the first 25 hours of the flood (period of high-water levels and the next water level dropping) the Safety Factor evaluated by limit equilibrium methods began to decrease, and reached the minimum value during the greatest seepage curve gradients at the time between 45 and 50 hours. Slope stability calculations by the limit equilibrium method were compared with the results of calculations performed by the SRM method, the values ​​of the Safety Factor and the way of their change during the flood evaluated by Ordinary and SRM methods almost coincide, which indicates the reliability of the results obtained by different methods of slope stability analysis