A multipurpose reservoir real-time operation model for flood control during typhoon invasion

Hsu, Nien‐Sheng; Wei, Chih-Chiang

doi:10.1016/j.jhydrol.2007.01.001

Cited by 115 publications

(49 citation statements)

References 14 publications

(11 reference statements)

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“…This paper employs the rolling horizon approach framework embedding real-time forecasts to determine the real-time release [Wasimi and Kitanidis, 1983;Unver and Mays, 1990;Labadie, 2004;Hsu and Wei, 2007;Zhao et al, 2011bZhao et al, , 2014. The entire flood season is divided into equally spaced n time-intervals, Dt.…”

Section: Two-stage Reservoir Operation Modelmentioning

confidence: 99%

“…Equation (9) implies that the release decision (R 2 ) is a random variable and should be adjusted to account for inflow forecast uncertainty if the ending storage S 2 needs to match a target [Datta and Burges, 1984;Hsu and Wei, 2007;Wei and Hsu, 2009]. Thus if inflow is overestimated, then release should be reduced; otherwise, more water than the expected will need to be released.…”

Section: Forecast Uncertainty and Release Uncertaintymentioning

confidence: 99%

“…Flood control requires a certain amount of empty storage during the flood season, while water conservation requires certain storage of water for current and future uses for irrigation, recreation, and hydropower generation. Many investigators have studied the trade-off between flood control and water conservation purposes [Croley et al, 1979;Krzysztofowicz and Duckstein, 1979;Datta and Houck, 1984;Simonovic and Burn, 1989;Jain et al, 1992;Hsu and Wei, 2007;Ngo et al, 2007Ngo et al, , 2008Wei and Hsu, 2009]. For example, Krzysztofowicz and Duckstein [1979] developed a preference criterion for reservoir flood control with consideration of water conservation under uncertain conditions.…”

Section: Introductionmentioning

confidence: 99%

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An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

Ding

Zhang

Peng

et al. 2015

Water Resources Research

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This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood-limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the trade-off between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress. The analysis is illustrated via a case study with a real-world reservoir in northeastern China.

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Section: Two-stage Reservoir Operation Modelmentioning

confidence: 99%

Section: Forecast Uncertainty and Release Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

Ding

Zhang

Peng

et al. 2015

Water Resources Research

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“…They may feel better protected legally if mandatory rules, such as the Dam Master Plan and the technical regulations, are followed [18]. Only a few of the cited papers analyze the short-term operation during flood events, with most being oriented to real-time operation, including inflow forecasting [19][20][21]. Thus, these methodologies, it could be argued, may be inappropriate in evaluating the performance of predefined strategies such as those considered in this work.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic-Multiobjective Comparison of User-Defined Operating Rules. Case Study: Hydropower Dam in Spain

Bianucci

Sordo‐Ward

Moralo

et al. 2015

Water

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Abstract:A useful tool is proposed in this paper to assist dam managers in comparing and selecting suitable operating rules. This procedure is based on well-known multiobjective and probabilistic methodologies, which were jointly applied here to assess and compare flood control strategies in hydropower reservoirs. The procedure consisted of evaluating the operating rules' performance using a simulation fed by a representative and sufficiently large flood event series. These flood events were obtained from a synthetic rainfall series stochastically generated by using the RainSimV3 model coupled with a deterministic hydrological model. The performance of the assessed strategies was characterized using probabilistic variables. Finally, evaluation and comparison were conducted by analyzing objective functions which synthesize different aspects of the rules' performance. These objectives were probabilistically defined in terms of risk and expected values. To assess the applicability and flexibility of the tool, it was implemented in a hydropower dam located in Galicia (Northern Spain). This procedure allowed alternative operating rule to be derived which provided a reasonable trade-off between dam safety, flood control, operability and energy production.

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“…Both ANNs and fuzzy theories are state-of-the-art technologies that try to mimic the human thinking process for learning similar strategies or experiences to make optimal decisions, and are well recognized for their outstanding abilities in modeling complex nonlinear systems such as precipitation estimation/prediction (Chiang et al, 2007), streamflow forecasting (Abrahart and See, 2002;Brath et al, 2002;Chiang et al, 2004;Dawson et al, 2002;Shrestha and Nestmann, 2009;Toth, 2009), reservoir operations (Chaves and Kojiri, 2007;Hsu and Wei, 2007;Mehta and Jain, 2009;Pinthong et al, 2009), prediction of water quality parameters (Sudheer et al, 2006;Tyagi et al, 2008), and pumping operations (Chang et al, 2008;Rao et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

Chiang

Chang

Tsai

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract.Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

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A multipurpose reservoir real-time operation model for flood control during typhoon invasion

Cited by 115 publications

References 14 publications

An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

Probabilistic-Multiobjective Comparison of User-Defined Operating Rules. Case Study: Hydropower Dam in Spain

Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

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