Abstract
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.
Highlights
Floods can cause enormous economic damage and human suffering, thence, vulnerable areas require control measures to reduce the impacts [1,2]
In order to evaluate model performance, the Bayesian Networks (BNets) model is compared to two standard models for dam flood operation: the Volumetric Evaluation Model (VEM) and the Mixed Integer Linear
The results of the Volumetric Evaluation Method (VEM) model set the baseline for comparison: any proposed model should at least improve the performance of the standard (a) is taken as the upper limit of model (b)performance
Summary
Floods can cause enormous economic damage and human suffering, thence, vulnerable areas require control measures to reduce the impacts [1,2]. Reservoirs play an essential role in mitigating the effects of floods [3,4,5,6]. The potential conflicts between flood control and the regular operation in multipurpose reservoirs imply a great challenge in the operation decisions [5,7,8,9]. There is still huge uncertainty regarding the proper operation of a reservoir for real-time flood control, that increases the risk in the discharge decision-making process [11,12]. The management of reservoirs for flood control in real-time has been historically addressed through curves derived from predefined rules obtained by means of simulation techniques [8], or by using programmed management methods, such as Volumetric Evaluation Method (VEM) [13], widely used
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