Real-time Flood Control Operation Research Articles

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.

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.

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 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.

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.

Application of an Optimization/Simulation Model for Real-Time Flood-Control Operation of River-Reservoirs Systems

Application of an Optimization/Simulation Model for Real-Time Flood-Control Operation of River-Reservoirs Systems

Development of an Optimization/Simulation Model for Real-Time Flood-Control Operation of River-Reservoirs Systems

Real-time optimal operation models for river-reservoir systems, unfortunately, are not widely available. Such models are still in their infancy perhaps due to the complexity of the application. This paper presents the development and testing of a methodology for determining reservoir release schedules before, during, and after an extreme flood event in real time. The problem is formulated as a real-time optimal control problem in which reservoir releases represent the decision variables. The model consists of five major components: (1) the U.S. Army Corps of Engineers (USACE) Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS), which simulates rainfall-runoff processes of watershed systems; (2) the U.S. Army Corps of Engineers Hydrologic Engineering Center - River Analysis System (HEC-RAS) for one-dimensional unsteady flow routing; (3) a reservoir release operation model; (4) a short-term rainfall forecasting model to project rainfall over the next few hours during a rainfall event; and (5) a genetic algorithm (GA) optimizer interfaced with the other components that determine the real time operation of a river-reservoir systems. An example application is used to test the development of the modeling framework, also illustrating the use of such a model. Each model component and its interface in the modeling framework was tested for quality assurance.

Risk analysis for the downstream control section in the real-time flood control operation of a reservoir

Many uncertainty factors are associated with the joint operation of a reservoir and its downstream river, which create risks in flood control decisions. Therefore, this paper proposes an analytical method for the estimation of the uncertainties and their risks in real-time flood control decisions. Three uncertainty factors, including reservoir discharge errors, forecasting errors of lateral inflows and river food routing errors are proposed and modeled as stochastic processes, and their internal transforming formulas are derived based on the theory of routing before combination. The definition and calculation formulas for the risks of each moment and the integrated risk of the entire flood process at the downstream flood control section are proposed by an analytical approach based on the combination theory of stochastic processes. The Dahuofang reservoir in northern China is selected as the study case. The results indicate that the risk of the flood peak is higher than that of other moments under the same controlled flood discharge and that the risk that arises from the uncertainties of the reservoir discharge and lateral inflow is decreased by the river storage function. Compared with the Monte Carlo method, the proposed method is effective and efficient for performing risk analysis of the downstream control section in the real-time flood control operation of a reservoir. The risk analysis results could provide important information regarding flood risks for the operators to implement flood control arrangements.

Risk Analysis for Real-Time Flood Control Operation of a Reservoir

AbstractThere are many uncertainties in real-time flood control operation of a reservoir, which create risks in flood control decision making. In this paper, three uncertainty factors—reservoir inflow-forecasting errors, outflow errors, and observation errors of reservoir storage capacity curve—are taken into account and quantified methods are proposed. With consideration of the three uncertainties and correlation between inflow-forecasting errors, reservoir water-level errors are derived using the stochastic differential equation of reservoir flood routing. Then the definition and calculation methods for flood risk at each moment and the integrated risk of the entire flood process are proposed. The Dahuofang reservoir in China is selected as the case study. The results shows that the risk resulting from the uncertainties is decreased by the reservoir flood regulation function and that the proposed method can provide a useful way to estimate the risks in real-time reservoir flood control.

Decomposition–coordination model of reservoir group and flood storage basin for real-time flood control operation

The research of joint optimization operation of complex flood control systems is still in the process of development. This paper introduces a decomposition–coordination model for solving the multi-objective optimization problem for real-time flood control operation in reservoir group and flood storage basin. The multi-objective programming is established for maximum safety of the reservoir group and minimum losses of flood storage basin, according to the real-time flood control requirements. Then, a third-order hierarchical optimization decomposition–coordination model is proposed for solving the multi-objective programming problem, based on the decomposition–coordination principle of large scale system theory. It takes advantage of an objective coordination method and model coordination method to accomplish global optimization and combines progressive optimality algorithm to solve the subsystem local optimization. Finally, the model is applied for simulating the storm flood in July 2007 in the middle reaches of the Huaihe River Basin in China. Results show that the proposed decomposition–coordination model can efficiently calculate the reservoir group optima release strategy and flood storage basin diversion process, and meet the safety discharge at the downstream control section.

Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system

This study presents a methodology to establish a set of optimal operation release rules which are tree-based rules for real-time flood control on a multipurpose multireservoir system. The derived rules can be used to determine the optimal real-time releases during flood periods. Steps of the proposed methodology involve: (1) collection of data, (2) building of flood database, (3) generation of optimal input–output patterns by running the flood control optimization model, (4) classification of training and testing data, (5) extraction of tree-based release rules for designed scenarios using the decision-tree algorithm (C5.0), (6) determination of optimal tree-based rules, (7) generation of the real-time forecast data by using the hydrological forecast model, (8) processing of reservoir real-time releases by simulating the reservoir real-time flood control operation, and (9) verification of the superior release rules through comparisons of tree-based rules, regression-based rules derived from a multiple-linear regression model and existing release rules. The developed methodology is applied to the Tanshui River Reservoir System in Taiwan to extract the decision trees for each scenario and then select the best ones with highest accuracy as the optimal tree-based rules. The derived optimal tree-based rules, regression-based rules and existing rules are compared by conducting the real-time operations in three historical typhoons, including Aere, Haima and Nock-ten in 2004. Results demonstrate that the solution using the derived tree-based rules have better performance than the regression-based rules and the existing rules in terms of reducing the peak stage at downstream control points, and meeting the target reservoir storage at the end of flood.