A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

Liu, Pan; Lin, Kairong; Xiao-jing, Wei

doi:10.1007/s00477-014-0986-0

Cited by 48 publications

(10 citation statements)

References 36 publications

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“…Hydrological forecasting, especially medium and long-term runoff forecasting, is an indispensable part of water resources management and water conservancy projects' operation [1][2][3]. Forecasting at different time scales can provide valuable information for flood control, power generation, water supply, and drought resistance [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Song

Liu

Sun

et al. 2020

Water

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Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.

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Section: Introductionmentioning

confidence: 99%

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Song

Liu

Sun

et al. 2020

Water

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“…Long-term hydrological prediction is of significance for water resource activities, such as reservoir operation [1][2][3][4][5], water resource planning [6][7][8][9], risk management [10][11][12][13], and urbanization [14,15]. Hence, hydrologic time-series forecasting, especially monthly inflow, has triggered great interest in hydrology and water resources fields [16,17].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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Abstract:Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. OPEN ACCESSWater 2015, 7 4478

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“…The two‐stage theory developed by Liu, Lin, and Wei () is extended to the aggregation system. The two‐stage flood risk analysis equation for aggregation reservoirs is as follows:

R_{k} ((), P) = \int P ((), S_{end, k}) R ((), S_{end, k}, P) italicdS = \sum_{q = 1}^{F} P ((), S_{end, k, q}) R ((), S_{end, k, q}, P)

where, R k ( P ) is the flood risk with a flood of frequency P in the k th aggregation reservoir, F is the number of flood events, S end , k , q is the storage at the end of the pre‐discharge period of the k th aggregation reservoir with the q th flood, P ( S end, k , q ) is the storage probability at the end of the pre‐discharge operation for the k th aggregation reservoir with the q th flood, and R ( S end, k , q , P ) is the relationship between the storage and the flood risk of the k th aggregation reservoir with the q th flood.…”

Section: Methodsmentioning

confidence: 99%

“…For the multi‐reservoir operation, Tan et al () proposed a model that considered the capacity compensation of two reservoirs and the spatial uncertainty of flood characterisation using a confidence level that was subjective. Liu, Guo, Li, Chen, and Li (); Liu, Lin, and Wei () developed a two‐stage risk analysis method that used a forecast horizon point to divide future times into two stages (hydrological forecast lead time and unpredicted time), and effectively calculated the flood risk for single reservoirs. However, the risk analysis was affected by the issue of dimensionality as the number of reservoirs in the multi‐reservoir system increased.…”

Section: Introductionmentioning

confidence: 99%

Determining dynamic water level control boundaries for a multi‐reservoir system during flood seasons with considering channel storage

Gong

Liu

Cheng

et al. 2019

J Flood Risk Management

Self Cite

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The dynamic control of reservoir water level during flood seasons (WLFS) allows a trade-off between flood control and water resources conservation. In this process, the WLFS dynamic control boundaries are key parameters. The channel flood routing affects the dynamic control operations. However, flood routing is seldom considered in multi-reservoir aggregation (a popular method for tackling the curse of dimensionality in multi-reservoir issues). To address this knowledge gap, this study conceptualises channel flood routing as a virtual channel storage reservoir (CSR) based on Muskingum flood routing in the aggregation method.The dynamic control boundaries for the multi-reservoir WLFS are then derived using the following aggregation-decomposition method: (a) the aggregation reservoir is created by considering channel storages, and the flood risk is quantified using pre-discharge rules; and (b) the aggregated reservoir storages are decomposed by maximising hydropower generation with constraints. The Xiluodu, Xiangjiaba and Three Gorges reservoir system on the Yangtze River, China, were selected for a case study. The annual power generation is 175.40 billion kWh when using static operation rules. With dynamic operation, the output would be 177.05 billion kWh if CSRs were not considered and 177.53 billion kWh if CSRs were considered. Thus, the proposed method can efficiently determine the dynamic control boundaries for the multi-reservoir WLFS. K E Y W O R D Saggregation-decomposition, channel storage reservoir, dynamic control boundary, multireservoir operation, water level during flood seasons

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A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

Cited by 48 publications

References 36 publications

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Determining dynamic water level control boundaries for a multi‐reservoir system during flood seasons with considering channel storage

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