Real-time Flood Control Research Articles

Perspectives on risk analysis and control for real-time operation of flood control systems

Flood control operation, a non-engineering measure, can efficiently manage flood disasters within a river basin. However, numerous uncertainties exit in the real-time operation of flood control systems, creating risks in decision-making. As an efficient tool to mitigate these risks, risk management has garnered increasing attention in real-time flood control operation. This communication offers a series of suggestions for future research concerning risk management in real-time flood control operation, including risk assessment, risk diagnosis, and risk control methods.

Many-Objective Hierarchical Pre-Release Flood Operation Rule Considering Forecast Uncertainty

Flood control operation of cascade reservoirs is an important technology to reduce flood disasters and increase economic benefits. Flood forecast information can help reservoir managers make better use of flood resources and reduce flood risks. In this paper, a hierarchical pre-release flood operation rule considering the flood forecast and its uncertainty information is proposed for real-time flood control. A many-objective optimization model considering the cascade reservoir’s power generation objective, flood control objective, and navigation objective is established. Then, a region search evolutionary algorithm is applied to optimize the many-objective optimization model in a real-world case study upstream of the Yangtze River basin. The optimization experimental results show that the region search evolutionary algorithm can balance convergence and diversity well, and the HV value is 40% higher than the MOEA/D algorithm. The simulation flood control results of cascade reservoirs upstream of the Yangtze River demonstrate that the optimized flood control rule can increase the average multi-year power generation of cascade reservoirs by a maximum of 27.72 × 108 kWh under the condition of flood control safety. The rules proposed in this paper utilize flood resources by identifying runoff forecast information, and pre-release to the flood limit level 145 m before the big flood occurs, so as to ensure the safety downstream and the dam’s own flood control and provide reliable decision support for reservoir managers.

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.

Dynamic Self-Adaptive Modeling for Real-Time Flood Control Operation of Multi-Reservoir Systems

In the real-time flood control operation of multi-reservoir systems, it is of great significance to establish a dynamic operating system with high efficiency based on the spatiotemporal variation of flood control situations. This paper proposes a self-adaptive modeling framework for real-time flood control operation of multi-reservoirs based on the cyber–physical system (CPS) theory. Firstly, the random flood samples considering the randomness of both space and magnitude are generated, and then the multi-reservoir real-time flood control hybrid operation (MRFCHO) model is established based on the dynamic identification of effective reservoirs. Then, the CPS theory is introduced to put forward the multi-reservoir real-time flood control hybrid operation cyber–physical system (MRFCHOCPS), which integrates real-time monitoring, control center, database, computation module, and communication network. Finally, the proposed framework is demonstrated in terms of accuracy, efficiency, and adaptability in real-time flood control operations. A case study of the multi-reservoir system upstream of the Lutaizi point in the Huaihe River basin in China reveals that (1) the equivalent qualified rate of the MRFCHO model is 84.9% for random flood samples; (2) the efficiency of solving the MRFCHO model is much higher than the efficiency of solving the MRFCJO model under the premise of ensuring the flood control effect, so it provides a reliable method for the real-time operation of basin-wide floods; (3) the MRFCHOCPS has good adaptability in real-time dynamic modeling and operation of large-scale multi-reservoir systems.

Risk analysis for the multi-reservoir flood control operation considering model structure and hydrological uncertainties

In the real-time flood control operation of a multi-reservoir system, the multi-reservoir real-time flood control hybrid operation (MRFCHO) model with reduced structure is an effective way to improve the efficiency, but the reduction of the model structure may cause greater flood risk. This paper focuses on this scientific problem and evaluates the reliability of the MRFCHO model in real-time flood control operation through risk analysis. Firstly, the flood risk is defined. Then, the Bayesian network risk analysis model is established through four parts: Bayesian network structure construction, training samples generation, parameter learning, and inference. This paper takes the flood control system in upstream of the Lutaizi point in the Huaihe River basin as a case study. The flood risk of the MRFCHO model caused by the model structure reduction is quantitatively described, the spatial distribution and propagation characteristics of flood risk are evaluated, and the flood risk caused by the double uncertainties of the model structure and flood forecasting is analyzed. Besides, the factors affecting the risk of the public flood control point are diagnosed. The results show that (1) the model structure reduction does not significantly increase the flood risk at the public flood control point, and there is no obvious propagation law of flood risk in space; (2) compared with only considering the model structure reduction, considering the double uncertainties of flood forecasting and model structure reduction has a 65% probability of increasing the risk; (3) Runoff from the Runheji point in the upper reaches of main stream and the lateral inflow of Lutaizi point are the main reasons for the risk of Lutaizi point.

Multistage Models for Flood Control by Gated Spillway: Application to Karkheh Dam

The paper demonstrates a simulation optimization framework for enhancing the real-time flood control with gated spillways at places where no flood forecasting data are available. A multiobjective modeling scheme is presented for the flood management in a gated spillway in which the operator may specify the priorities on floods based on their different return periods. Two different operation strategies were devised. Both operating strategies employ ten-stage policies, which rely on the reservoir water level as the input data. The second strategy benefits from both the observed reservoir water level and the flood peak. The optimal values of the models’ parameters were obtained using a genetic algorithm. This is a novel approach because none of its policies needs flood forecasting data, thus, making them adaptable to any flood with any return period. To evaluate the performances of the proposed models, the flood control through a gated spillway of the Karkheh reservoir was considered, where flood hydrographs with different return periods were routed through the reservoir.

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.

Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties

Uncertainties in flood forecasts that result from error propagation in meteorological–hydrological simulation processes present spatial and temporal dependences that are the principal sources of risk to real-time flood control in a multireservoir system. Risk analysis models that neglect such dependences potentially yield biased results regarding flood control operations. To enhance the accuracy of flood risk analysis and inform decision making regarding real-time flood control for a multireservoir system, this study proposed an integrated flood risk identification, analysis, and diagnosis model framework that incorporates the multidimensional dependences of forecast uncertainties. The framework includes an uncertainty identification model that uses the copula function to characterize the joint probability function of the mixed dependent uncertainties. It also incorporates a real-time flood risk analysis module that couples simulation and optimization to convert the complex multiobjective operation under uncertainty into a tractable model capable of flexibly evaluating individual and joint flood risks. The third component is a risk diagnosis module that calculates risk contributions using conditional probability, and identifies stable and vulnerable reservoirs within the system to alter operational decisions toward risk reduction. Methodologies were verified through application to a mixed four-reservoir system in the Pi River Basin, China. The principal findings were as follows. (1) Positive-dominated dependences of forecast uncertainties were identified because of error propagation through the meteorological and rainfall–runoff forecasts within the spatial and temporal continuity connection, and the copula function accurately described and quantified the multidimensional temporal and spatial dependences of the uncertainties. (2) In neglecting the spatial dependences of forecast uncertainties, the joint flood risk of the reservoir system was underestimated; in ignoring high-order temporal dependences, individual flood risk was underestimated owing to biased estimation in error variance. (3) Systematic reorganization of the operational strategies of diagnosed stable and vulnerable reservoirs could provide guidance for reduction and homogenization of flood risks. The proposed framework could be used to improve the accuracy of flood risk analysis and support reliable real-time flood control operation of a multireservoir system.

Probabilistic Model for Real-Time Flood Operation of a Dam Based on a Deterministic Optimization Model

This paper presents a real-time flood control model for dams with gate-controlled spillways that brings together the advantages of an optimization model based on mixed integer linear programming (MILP) and a case-based learning scheme using Bayesian Networks (BNets). A BNet model was designed to reproduce the causal relationship between inflows, outflows and reservoir storage. The model was trained with synthetic events generated with the use of the MILP model. The BNet model produces a probabilistic description of recommended dam outflows over a time horizon of 1 to 5 h for the Talave reservoir in Spain. The results of implementing the BNet recommendation were compared against the results obtained while applying two conventional models: the MILP model, which assumes full knowledge of the inflow hydrograph, and the Volumetric Evaluation Method (VEM), a method widely used in Spain that works in real-time, but without any knowledge of future inflows. In order to compare the results of the three methods, the global risk index (Ir) was computed for each method, based on the simulated behavior for an ensemble of hydrograph inflows. The Ir values associated to the 2 h-forecast BNet model are lower than those obtained for VEM, which suggests improvement over standard practice. In conclusion, the BNet arises as a suitable and efficient model to support dam operators for the decision making process during flood events.

Research on joint flood control operation rule of parallel reservoir group based on aggregation–decomposition method

With the continuous construction of reservoir group systems and the improvement in flood control objectives in China, a demand has arisen for joint flood control operations of reservoir groups in river basins. However, the existing conventional flood control operation rule (CFCOR) of most reservoirs are based generally on a simple discriminant and there is a lack of coordination among the reservoir group. Further, owing to a shortage of high-precision, long-term forecasting schemes and forecasting professionals for reservoirs in small and medium river basins, it is difficult to obtain acceptable results when applying the forecasting-operation mode. Therefore, an optimal joint flood control operation rule (OJFCOR) of parallel reservoir groups is proposed in this study based on the Aggregation–Decomposition method (AGDP). This proposed rule comprises the aggregated reservoir flood control operation chart and the decomposition strategy named occupied flood regulation storage capacity ratio method (OFRSCR). We applied the simulation-optimisation method (SLOS) nested annealing accuracy penalty function method-adaptive multi-variant particle swarm optimisation (AAPF-AMPSO) to optimise OJFCOR, with the objective function being the maximum average peak-cutting rate of the flood control section. A case study was conducted with a parallel reservoir group located in the upper reaches of the Lijiang River basin (URLRB). Our results showed that, compared with the widely used inflow ratio method (IR), the performance of OFRSCR was a more suitable decomposition strategy and superior in maintaining the synchronisation of the parallel reservoir group. Further, OJFCOR showed a satisfactory peak-cutting effect on floods with different rainstorm centres and flood magnitudes, with the average flood peak-cutting rate of 37.68%, which is superior to those of CFCOR (25.26%) and DP (32.69%), but inferior to that of POA-DPSA (45.23%). Besides strong peak-cutting ability and strong adaptability, OJFCOR also has advantages of smooth outflow process and being convenient for professional operators. Generally, the OJFCOR proposed in this research was found reasonable and effective, and it was shown suitable for practical real-time flood control operations and reducing the effects of flood disasters.

Research on the Co-occurrence Characteristics of the Floods Between Lower Reaches of Jinsha River and the Chuanjiang River

In order to provide a theoretical basis for real-time flood control, reduce the flood disaster in the lower reaches of Jinsha River and the upstream Yangtze River (the section in Sichuan Province, also known as the Chuanjiang River), as well as alleviate flood control pressure of the downstream Three Gorges Project flood encounter characteristics between the lower reaches of Jinsha River and the Chuanjiang River were discussed in detail based on Copula Function Method. AIC criterion and OLS criterion were applied in the paper to evaluate the fitting effect of four Archimedean Copula function models, and Frank Copula function with the best effect was selected for the flood encounter analysis subsequently. The result shows that: the proportion of the maximum 3d, 7d, 15d, 30d flood volume of the lower reaches of Jinsha River in the composition of the Chuanjiang River increases in sequence. Frank Copula function has very small errors in the flood encounter problems of the lower reaches of Jinsha River and Chuanjiang River, with higher credibility, its calculation results are consistent with the flood encounter laws of the two rivers. The frequency of occurrence of the largest flood peak in the two rivers in different months is significantly different. The co-occurrence probability of floods with peak flow greater than 50-year flood for the two rivers is 0.15%. Therefore, by taking full advantages of the flood co-occurrence characteristics, it’s extremely practicable to reduce not only the flood control pressure of Chuanjiang River but also the downstream Yangtze with the cascade reservoir operation in the lower reaches of Jinsha River.

Intelligent identification of effective reservoirs based on the random forest classification model

In the real-time operation of a flood control system, identifying effective reservoirs accurately and adaptively is the premise of establishing a multi-reservoir real-time flood control hybrid operation (MRFCHO) model. The existing effective reservoir intelligent reasoning (ERIR) method is based on the inference rules, which causes the identification results to be greatly influenced by the training samples. This paper establishes a random forest classification (RFC) model for identifying effective reservoirs to solve this problem. The performance of the RFC model is evaluated in terms of model stability and model accuracy, and compared with the ERIR model and other machine learning (ML) models. First, different flood samples are used to establish the models to verify the model stability. Then, the expected total cost under cross-validation is taken as the index to evaluate the classification accuracy. Finally, the average increase of expected total cost with cross-validation is proposed to evaluate the criteria importance and analyze the sensitive factors that affect the classification accuracy of the RFC model. The proposed method is applied to a multi-reservoir system in the Huaihe River basin in China. The results indicate that the RFC model has the characteristic of high classification accuracy, low sensitivity to flood samples and high stability. It displays more dominance in the dynamic identification of effective reservoirs compared to other models.

A multi-objective risk management model for real-time flood control optimal operation of a parallel reservoir system

Many uncertainties are associated with real-time reservoir flood control operation and introduce risks for decision making. A multi-objective risk management model is proposed to optimize the real-time flood control operation of a parallel reservoir system during flood season. The model considers the combined impact of reservoir inflow and lateral inflow uncertainties and flood control operation risks. It consists of three key components: a risk optimal operation submodel, a risk assessment submodel, and an optimization submodel. The risk optimal operation submodel takes into account the uncertainties and establishes an operation model that considers two competing objectives of minimizing the risk of upstream flooding and minimizing the risk of downstream flooding. The risk assessment submodel calculates the risks based on a stochastic differential equation (SDE). The final optimization submodel embeds the risk assessment model in the risk optimal operation model and is solved by the Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The proposed methodology is applied to a real flood control system in the middle reaches of the Huaihe River basin in China. The results show that the developed multi-objective risk management model can provide operation schedules that satisfy flood control objectives and simultaneously minimize the overall risks. The Pareto front of the proposed objectives demonstrates the tradeoff among the competing objectives. The model can be used as a decision-making tool for conducting risk management for real-time reservoir flood control operation during flood season. The decision makers can choose the operation schedule according to their risk preference and updated hydrological information.

Reduction of the Criteria System for Identifying Effective Reservoirs in the Joint Operation of a Flood Control System

The accurate and efficient identification of effective reservoirs plays an important role in the real-time flood control operation of multireservoir systems. The redundancy of the initial criteria system affects the efficiency of identifying effective reservoirs. In this paper, a criteria reduction model based on the equivalence relation of rough set theory is proposed to solve this problem. Correspondingly, the decision rules are derived for identifying effective reservoirs. In addition, the reduction result of rough set theory is compared with the results of correlation analysis-principal component analysis (CA-PCA), vague set theory and back-propagation (BP) neural network methods. The proposed methodology is applied to the flood control system in the upper and middle reaches of the Huaihe River basin in China. The results show that the dimensionality of the initial criteria system is reduced and that the reduction result is reasonable. The proposed reduction model is an effective tool to reduce the initial criteria system for effective reservoir identification in real-time flood control operation.

Real-Time River Flood Control under Historical and Future Climatic Conditions: Flanders Case Study

AbstractModel predictive control (MPC) has shown to be an efficient technique for real-time flood control. The evaluation of the control performance is, however, typically restricted to a limited s...

Real-time reservoir flood control operation for cascade reservoirs using a two-stage flood risk analysis method

The real-time operation of multi-reservoir systems is a vital issue in the field of reservoir management. The uncertainty caused by inflow forecasting means that risk analysis is necessary for such real-time operation. However, differences in the length of the forecast periods for different reservoirs in the system are seldom considered in the risk analysis of multi-reservoir systems. This paper presents a two-stage flood risk analysis method of multi-reservoir systems that takes the differences in the length of forecast lead-times into consideration. The aim of the proposed method is to evaluate the uncertainty of the flood forecasting by dividing the operation horizon into the forecast lead-time and the beyond-forecast time period. The risk within the forecast lead-time is estimated by counting the frequency of failure numbers among all scenarios with the help of the scenario-based forecasts. The risk beyond the forecast time period is determined using reservoir flood routing with the design flood hydrographs, which are selected according to the differences in the length of the forecast periods between any two reservoirs. The proposed two-stage risk analysis method is verified using stochastic simulations method based on Monte Carlo sampling. A real-time flood control operation model is established by taking the proposed two-stage risk analysis method as a constraint. China’s Ankang-Danjiangkou Cascade Reservoirs are considered as a case study, and the results indicate that the proposed real-time operation model can increase hydropower generation by 0.22 billion kWh during the summer flood season without increasing the flood risk in the multi-reservoir system. The proposed method enhances our understanding of risk management for the real-time flood control operations in multi-reservoir systems.

Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network

This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.

Model uncertainty reduction for real-time flood control by means of a flexible data assimilation approach and reduced conceptual models

Recently, a combination of model predictive control and a reduced genetic algorithm (RGA-MPC) has shown to be an efficient control technique for real-time flood control, making use of fast conceptual river models. This technique was so far only tested under ideal circumstances of perfect model predictions. Prediction errors originating from hydrodynamic model mismatches, however, result in a deterioration of the real-time control performance. Therefore, this paper presents two extensions of the RGA-MPC technique. First, a new type of conceptual model is introduced to further increase the computational efficiency. This reduced conceptual model is specially tailored for real-time flood control applications by eliminating all unnecessary intermediate calculations to obtain the flood control objectives and by introducing a new transport element by means of flow matrices. Furthermore, the RGA-MPC technique is extended with a flexible data assimilation approach that analyzes the past observed errors and applies an appropriate error prediction scheme. The proposed approach largely compensates for the loss in control performance due to the hydrodynamic model uncertainty.

Real-Time Flood Control by Tree-Based Model Predictive Control Including Forecast Uncertainty: A Case Study Reservoir in Turkey

Optimal control of reservoirs is a challenging task due to conflicting objectives, complex system structure, and uncertainties in the system. Real time control decisions suffer from streamflow forecast uncertainty. This study aims to use Probabilistic Streamflow Forecasts (PSFs) having a lead-time up to 48 h as input for the recurrent reservoir operation problem. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). Deterministic Streamflow Forecasts (DSFs) are provided by applying random perturbations on perfect data. PSFs are synthetically generated from DSFs by a new approach which explicitly presents dynamic uncertainty evolution. We assessed different variables in the generation of stochasticity and compared the results using different scenarios. The developed real-time hourly flood control was applied to a test case which had limited reservoir storage and restricted downstream condition. According to hindcasting closed-loop experiment results, TB-MPC outperforms the deterministic counterpart in terms of decreased downstream flood risk according to different independent forecast scenarios. TB-MPC was also tested considering different number of tree branches, forecast horizons, and different inflow conditions. We conclude that using synthetic PSFs in TB-MPC can provide more robust solutions against forecast uncertainty by resolution of uncertainty in trees.

A Risk-Based Model for Real-Time Flood Control Operation of a Cascade Reservoir System under Emergency Conditions

Real-time flood control operations of a cascade reservoir system under emergency conditions can reduce the social and economic loss caused by natural disasters. This paper proposes a risk-based model for real-time flood control operation of reservoirs under emergency conditions and uncertainties. The proposed model consists of three modules: emergency scenarios establishing, Monte Carlo simulations, and risk analysis. The emergency scenarios considered are earthquakes, extreme floods and failure of the spillways of a reservoir. The uncertainty factor considered is the forecast error of reservoir inflows, arising from model structural uncertainty and parameter estimating. The Monte Carlo simulations conduct the real-time flood control operation of reservoirs considering the proposed emergency events and uncertainties. The module of risk analysis performs the assessment of the operation schedules and calculates the risk of dam overtopping, based on the results from Monte Carlo simulations. The proposed model is applied to a cascade reservoir system in the upper reaches of Daduhe river basin in China. The results show that the maximum initial water level of the Shuangjiangkou reservoir is 2447 m a.s.l. (meters above sea level) using the release capacity model and is 2444.5 m a.s.l. using the command model under the scenario of upstream dam break. The integrated risk of the reservoir increases with the initial water level and the uncertainty degree of the reservoir inflows. The decision-makers can choose the operation models according to the actual initial water level of the reservoir under different emergency scenarios.