Real-time Flood Control Research Articles

Combining Model Predictive Control with a Reduced Genetic Algorithm for Real-Time Flood Control

AbstractThis paper presents a novel meta-heuristic approach for real-time flood control. The approach combines model predictive control (MPC) with a reduced genetic algorithm (RGA) to quickly find ...

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

Dealing with rainfall forecast uncertainties in real-time flood control along the Demer river

Real-time Model Predictive Control (MPC) of hydraulic structures strongly reduces flood consequences under ideal circumstances. The performance of such flood control may, however, be significantly affected by uncertainties. This research quantifies the influence of rainfall forecast uncertainties and related uncertainties in the catchment rainfall-runoff discharges on the control performance for the Herk river case study in Belgium. To limit the model computational times, a fast conceptual model is applied. It is calibrated to a full hydrodynamic river model. A Reduced Genetic Algorithm is used as optimization method. Next to the analysis of the impact of the rainfall forecast uncertainties on the control performance, a Multiple Model Predictive Control (MMPC) approach is tested to reduce this impact. Results show that the deterministic MPC-RGA outperforms the MMPC and that it is inherently robust against rainfall forecast uncertainties due to its receding horizon strategy.

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.

Combine Evolutionary Optimization with Model Predictive Control in Real-time Flood Control of a River System

In order to establish successful flood control strategies to prevent or alleviate severe flood damages, real-time optimization-based control can be a supplementary strategy, besides setting operating rules (regulations) for the hydraulic structures. This research combines evolutionary optimization, by means of a Genetic Algorithm (GA), with the Model Predictive Control (MPC) technique to develop and test a real-time flood control method for the 12 gated weirs in the Belgian case study of the river Demer. The evolution of this method is also the main contribution of this study. The combination of GA with MPC allows coping with the highly nonlinear system behaviour and local minimum problems. The system searches for better control actions by minimizing a cost function while at the same time avoiding violation of the defined constraints. The optimization results testify that the system is able to assist the current regulation strategies that are based on fixed regulation rules (three-position controller).

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.

Multi-phase intelligent decision model for reservoir real-time flood control during typhoons

This study applies an Adaptive Network-based Fuzzy Inference System (ANFIS) and a Real-Time Recurrent Learning Neural Network (RTRLNN) with an optimized reservoir release hydrograph using Mixed Integer Linear Programming (MILP) from historical typhoon events to develop a multi-phase intelligent real-time reservoir operation model for flood control. The flood control process is divided into three stages: (1) before flood (Stage I); (2) before peak flow (Stage II); and (3) after peak flow (Stage III). The models are then constructed with either three phase modules (ANFIS-3P and RTRLNN-3P) or two phase (Stage I + II and Stage III) modules (ANFIS-2P and RTRLNN-2P). The multi-phase modules are developed with consideration of the difference in operational decision mechanisms, decision information, release functions, and targets between each flood control stage to solve the problem of time-consuming computation and difficult system integration of MILP. In addition, the model inputs include the coupled short lead time and total reservoir inflow forecast information that are developed using radar- and satellite-based meteorological monitoring techniques, forecasted typhoon tracks, meteorological image similarity analysis, ANFIS and RTRLNN. This study uses the Tseng-Wen Reservoir basin as the study area, and the model results showed that RTRLNN outperformed ANFIS in the simulated outcomes from the optimized hydrographs. This study also applies the models to Typhoons Kalmaegi and Morakot to compare the simulations to historical operations. From the operation results, the RTRLNN-3P model is better than RTRLNN-2P and historical operations. Further, because the RTRLNN-3P model combines the innovative multi-phase module with monitored and forecasted decision information, the operation can simultaneously, effectively and automatically achieve the dual goals of flood detention at peak flow periods and water supply at the end of a typhoon event.

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.

Development of a model-based flood emergency management system in Yujiang River Basin, South China

Flooding is the most frequent disaster in China. It affects people’s lives and properties, causing considerable economic loss. Flood forecast and operation of reservoirs are important in flood emergency management. Although great progress has been achieved in flood forecast and reservoir operation through using computer, network technology, and geographic information system technology in China, the prediction accuracy of models are not satisfactory due to the unavailability of real-time monitoring data. Also, real-time flood control scenario analysis is not effective in many regions and can seldom provide online decision support function. In this research, a decision support system for real-time flood forecasting in Yujiang River Basin, South China (DSS-YRB) is introduced in this paper. This system is based on hydrological and hydraulic mathematical models. The conceptual framework and detailed components of the proposed DSS-YRB is illustrated, which employs real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, data assimilation methods, and reservoir operational scenario analysis. Multi-tiered architecture offers great flexibility, portability, reusability, and reliability. The applied case study results show the development and application of a decision support system for real-time flood forecasting and operation is beneficial for 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.

Model Conceptualization Procedure for River (Flood) Hydraulic Computations: Case Study of the Demer River, Belgium

Model-supported real-time flood control requires the development of effective and efficient hydraulic models. As large numbers of iterations are to be executed in optimization procedures, the hydraulic model needs to be computationally efficient. At the same time, it is also required to generate high-accuracy results. Therefore, an identification and calibration procedure was developed for the purpose of having this conceptual model built up and calibrated based on a limited number of simulations with a more detailed full hydrodynamic model. The performance of the conceptual model was evaluated for historical events under different regulation conditions. Robustness test results show close agreement, with Nash-Sutcliffe Efficiency values higher than 0.90. In addition, it is found that the conceptual model is capable of accomplishing simulation of historical flood events within few seconds. That is much faster than the detailed full hydrodynamic model, which enables the conceptual model to be applied for real-time flood control.

Development of optimisation-based DSS for flood control

A decision support system (DSS) has been developed for integrated, real-time flood control in a multi-reservoir system. The DSS integrates an optimisation module with a geographic information system, and a graphical dialogue interface allowing effective use by system operators. A neurodynamic programming technique, which overcomes numerous limitations of stochastic dynamic programming, is introduced for determining the optimal release policy under flood conditions. The method is illustrated by a real-world case study.

A hydrographical network optimization under flood conditions

AbstractA decision support system (DSS) is developed for integrated, real-time flood control in a multi-reservoir system. The DSS integrates a geographic information system (GIS) with an optimization module, and a graphical dialog interface to allow an effective use by system operators. A Neuro-dynamic programming technique (NDP), which overcomes numerous limitations of stochastic dynamic programming (SDP), is introduced to determine the optimal release policy under flood conditions. Results obtained in a real-world case study are finally presented.

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.

Guiding rational reservoir flood operation using penalty-type genetic algorithm

Real-time flood control of a multi-purpose reservoir should consider decreasing the flood peak stage downstream and storing floodwaters for future usage during typhoon seasons. This study proposes a reservoir flood control optimization model with linguistic description of requirements and existing regulations for rational operating decisions. The approach involves formulating reservoir flood operation as an optimization problem and using the genetic algorithm (GA) as a search engine. The optimizing formulation is expressed not only by mathematical forms of objective function and constraints, but also by no analytic expression in terms of parameters. GA is used to search a global optimum of a mixture of mathematical and nonmathematical formulations. Due to the great number of constraints and flood control requirements, it is difficult to reach a solution without violating constraints. To tackle this bottleneck, the proper penalty strategy for each parameter is proposed to guide the GA searching process. The proposed approach is applied to the Shihmen reservoir in North Taiwan for finding the rational release and desired storage as a case study. The hourly historical data sets of 29 typhoon events that have hit the area in last thirty years are investigated bye the proposed method. To demonstrate the effectiveness of the proposed approach, the simplex method was performed. The results demonstrated that a penalty-type genetic algorithm could effectively provide rational hydrographs to reduce flood damage during the flood operation and to increase final storage for future usages.

Optimal Control of Open-Channel Flow Using Adjoint Sensitivity Analysis

An optimal flow control methodology based on adjoint sensitivity analysis for controlling nonlinear open channel flows with complex geometries is presented. The adjoint equations, derived from the nonlinear Saint-Venant equations, are generally capable of evaluating the time-dependent sensitivities with respect to a variety of control variables under complex flow conditions and cross-section shapes. The internal boundary conditions of the adjoint equations at a confluence (junction) derived by the variational approach make the flow control model applicable to solve optimal flow control problems in a channel network over a watershed. As a result, an optimal flow control software package has been developed, in which two basic modules, i.e., a hydrodynamic module and a bound constrained optimization module using the limited-memory quasi-Newton algorithm, are integrated. The effectiveness and applicability of this integrated optimal control tool are demonstrated thoroughly by implementing flood diversion controls in rivers, from one reach with a single or multiple floodgates (with or without constraints), to a channel network with multiple floodgates. This new optimal flow control model can be generally applied to make optimal decisions in real-time flood control and water resource management in a watershed.

Hierarchical Control Structures for Real Time Scheduling of Releases in a Multireservoir System During Floods

The paper presents two hierarchical control structures for real-time flood control in a multireservoir system. The aim of the control is to minimize the flood damages which are related to peak water levels at the important locations situated downstream the reservoirs. Water releases from the reservoirs are adjusted by the local operators while the objective of the central authority is to modify the release patterns (in case of the first control structure) or the local performance functions (in case of the second structure) so as to achieve his overall goals. The decision mechanism of the central dispatcher is based upon fast simulation of the system operation under different flood scenarios. Application of the proposed control structures to the case study multireservoir system is described together with selected simulation results.

Hierarchical Structure with Repetitive Goal Coordination for Real-time Flood Control in a Multireservoir System

The paper presents a hierarchical control structure for real time flood control in a multireservoir system. The aim of the structure is to minimize the flood damages which depend on the peak water levels at the most important locations situated far downstream the reservoirs. Water releases form the individual reservoirs are adjusted by the local operators while the objective of a central authority is to modify local performance functions so as to achieve his overall goals. Due to highly uncertain forecasting of future inflows the decision mechanism of a central dispatcher is based upon fast simulation of the system operation under different flow scenarios. An application of the control structure to a case study multireservoir system is shortly described together with some selected simulation results.