A Forecast-Based Operation(FBO) Mode for Reservoir Flood Control Using Forecast Cumulative Net Rainfall

Xiao, Wang; Liu, Zhao; Wei-bo, Zhou; Jia, Zhang; You, Qinglong

doi:10.1007/s11269-019-02267-y

Cited by 11 publications

(2 citation statements)

References 23 publications

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“…Principal component analysis (PCA) was used to reduce a large number of input variables to a manageable number [33,34]. PCA transforms a collection of correlated variables into a collection of principal components (uncorrelated variables).…”

Section: Data Preprocessing and Input Selectionmentioning

confidence: 99%

Rainfall modeling using two different neural networks improved by metaheuristic algorithms

Sammen,

Kisi,

Ehteram

et al. 2023

Environ Sci Eur

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Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models’ performance. The results indicated that the MLP–HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values were 0.90–0.23, 0.83–0.29, 0.85–0.25, 0.87–0.27, 0.81–0.31, and 0.80–0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO during testing processes and the corresponding NSE–PBIAS values were 0.92–0.22, 0.85–0.28, 0.89–0.26, 0.91–0.25, 0.83–0.31, 0.82–0.32, respectively. Based on the outputs of the MLP–HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP–HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP–HGSO was selected as an effective rainfall prediction model.

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Section: Data Preprocessing and Input Selectionmentioning

confidence: 99%

Rainfall modeling using two different neural networks improved by metaheuristic algorithms

Sammen,

Kisi,

Ehteram

et al. 2023

Environ Sci Eur

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“…The study was located at the Shimen Dam on the Tanshui River in Taiwan, is used to determine the optimal outflow by using a decision tree algorithm to obtain the optimal dam operating rules for three stages. Wang et al, (2019) 2017) used optimal reservoir operations in two stages to obtain various types of operating rules according to flood control reservoirs and erratic predictions. As we consider the proposed single reservoir flood control operating chart discussed above is logical and effective, so it can be considered that these methods are part of the joint flood control operating rules of parallel dams.…”

Section: Introductionmentioning

confidence: 99%

Flood Control Rule in Bintang Bano Reservoir

Afifah,

Sangkawati,

Suripin

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Flood is a disaster that occur since ancient times until now, and even in the future. Flood can be caused by nature, humans, or both. Constructing new dams are a good method to reduce flood and losses caused by floods, one of them is Bintang Bano Dam. The purpose of this study is to analyze the capacity of the gated spillway and ungated spillway, so that the allowable return period of discharge can be estimated through the both spillways. This research activity divided into 4 (four) stages are: the identification and data processing stage, the rainfall-runoff modeling stage, the reservoir flood routing, and the simulation of the operation of opening gates on both spillways. The gated spillway can be operated at all return periods (2 years to PMF). Ungated spillway can only overflow discharge with ≥ 100 years return period. During the return period 2 years to 50 years, no runoff occurs through the ungated spillway. For the return period of 100 years to 1000 years there is runoff through the ungated spillway without any gate openings. It is recommended for floods with a return period of 2 years to 50 years, with upstream and downstream conditions of rainfall, the gate can be closed. But for floods with 100 years to 1000 years return periods, the discharge automatically overflows through the ungated spillway cannot be opened/damaged.

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An Analytical Risk Analysis Method for Reservoir Flood Control Operation Considering Forecast Information

Ning

Ding

Liang

et al. 2021

Water Resour Manage

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A Forecast-Based Operation(FBO) Mode for Reservoir Flood Control Using Forecast Cumulative Net Rainfall

Cited by 11 publications

References 23 publications

Rainfall modeling using two different neural networks improved by metaheuristic algorithms

Rainfall modeling using two different neural networks improved by metaheuristic algorithms

Flood Control Rule in Bintang Bano Reservoir

An Analytical Risk Analysis Method for Reservoir Flood Control Operation Considering Forecast Information

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