Real-time Reservoir Optimal Operation for Flood and Sedimentation Control Considering Turbidity Constraints during Typhoon Invasion
Date Issued
2014
Date
2014
Author(s)
Huang, Chien-Lin
Abstract
This study develops two multi-layer reservoir conjunctive release operation models (RTRLNN-based FSC model and ANFIS-based FSC model) to determine the optimal real-time releases during typhoon invasions for the Shihmen Reservoir basin in Taiwan, taking into consideration turbidity constraints, flood control, and sedimentation control. The study can be divided into model construction and real-time operational simulation. The models consist of a forecast model component, an optimization model component, and an operational decision model component. The decision model is composed of the developed simulation-analysis approach and optimization of the operation hydrograph. The purpose of the simulation-analysis approach is to apply the US EPA WASP-based fluid dynamic sediment concentration simulation model with the developed extracting method of ideal releasing practice to search for the initial solution of optimization and construct the sediment concentration simulation models (ANFIS-based and RTRLNN-based) embedded in the optimization model. The optimization model is solved by tabu search and the optimized releasing operation hydrograph is used for the construction of the decision model. The developed short lead-time and total reservoir inflow forecast models (ANFIS-based and RTRLNN-based) and the real-time revised quantitative precipitation forecast model based on typhoon central location (RTR-TCL-QPF) are embedded in the operational decision model. The forecasted typhoon track information and the duration between the current time and each flood control stage from the Taiwan Central Weather Bureau (CWB) are entered into the operation model for release decision-making. Finally, this study assesses the quality of the decision model according to the outcome of real-time operation.
After applying the operational decision model on the Shihmen Reservoir basin, the results can be concluded as follows: (1) Under the synthesized simulation using key input of future total precipitation and flooding duration with other hydrometeorological factors, the accuracy and stability of the RTRLNN-based total reservoir inflow forecast model is better than the ANFIS-based model; (2) Error toleration and adaption are superior in ANFIS, therefore, the ANFIS-based sediment concentration model better simulates the mechanisms and characteristics of sediment transport than RTRLNN; (3) RTRLNN owns a real-time recurrent deterministic routing mechanism, allowing for better simulation under fewer data samples. Hence, the multi-layer release operation outcome evaluated by RTRLNN-based FSC model is better than ANFIS-FSC model; (4) This study couples the developed forecast model into the decision model to proceed with real-time operation of the Shihmen reservoir during Typhoon Fung-Huang and Typhoon Morakot. The inputs of the decision model include short lead-time, total reservoir inflow, the difference between current storage and maximum storage, the difference between current storage and target full storage, flooding duration between current time and each flood control stage, and real-time observed precipitation, reservoir inflow, sediment concentration of each releasing outlet and Yuan-Shan weir, downstream channel water level at Shan-Yin bridge, and multi-layer reservoir release. The model outputs are real-time evaluated multi-layer reservoir release. Results show that the operational outcome of the developed decision model is better than the historical operation according to the four assessment index: maximum sediment concentration of Yuan-Shan weir, sediment removed ratio, highest water level at Shan-Yin Bridge, and final stored reservoir water level.
Subjects
水庫防洪與防淤
即時操作
取水濁度
模擬-優選
調適性網路模糊推論系統
即時回饋類神經網路
Type
thesis
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