A case study for ANN-based rainfall–runoff model considering antecedent soil moisture conditions in Imha Dam watershed, Korea

Kang, Boosik; Ku, Young Hun; Kim, Jae Won

doi:10.1007/s12665-015-4117-0

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…Table 1 contains calculated correlation coefficients, all of which were significant with p -value < 0.05, therefore all those subsets were used as explanatory variables. According to the literature [22,23] using meteorological parameters such as precipitation, temperature or humidity in hydrological forecasting models, based on flow hydrodynamic transformation between neighbouring gauges does not lead to an improvement in accuracy.…”

Section: Forecasting Models -Resultsmentioning

confidence: 99%

Forecasting the Nysa Kłodzka flow rate in order to predict the available flow for a run-off-river (ROR) power plant

Jurasz

Wdowikowski

2017

E3S Web Conf.

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Abstract. Hydroelectricity is generally perceived as a stable and predictable power source. However ROR power plant without reservoir energy output is mainly driven by changing flow rate. This study applies artificial neural networks to create flow rate forecasts with one hour lead time. Forecasting models were built for Nysa Kłodzka catchment which possesses significant potential for new hydropower plants development as well as leads to frequent floods. The best of the obtained model gives satisfactory results both in terms of root mean square error (0.6379 m 3 /s) as well as Nash-Sutcliffe performance indicator (0.9978). Obtained results were compared with currently used forecasting models and were proven to be superior.

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Section: Forecasting Models -Resultsmentioning

confidence: 99%

Forecasting the Nysa Kłodzka flow rate in order to predict the available flow for a run-off-river (ROR) power plant

Jurasz

Wdowikowski

2017

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…In addition, to further compare the performance of the models, root mean square error (RMSE), mean absolute error (MAE), and determination coefficients (R 2 ) are used (Kang et al, 2015;Mohan et al, 2018). The results of the relevant indicators are shown in Table 4.…”

Section: Accuracy Of Different Learning Modelsmentioning

confidence: 99%

A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer

Liu

et al. 2022

Front. Energy Res.

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Phase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power systems with PST. In order to find a more efficient power flow optimization method, an improved genetic algorithm including a data-driven module is proposed. This method uses the deep belief network (DBN) to train the sample set of the power flow and obtains a high-precision proxy model. Then, the calculation of the DBN model replaces the traditional adaptation function calculation link which is very time-consuming due to a great quantity of AC power flow solution work. In addition, the sectional power flow reversal elimination mechanism in the genetic algorithm is introduced and appropriately co-designed with DBN to avoid an unreasonable power flow distribution of the grid section with PST. Finally, by comparing with the traditional model-driven genetic algorithm and traditional mathematical programming method, the feasibility and the validity of the method proposed in this paper are verified on the IEEE 39-node system.

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“…The reservoir has a storage capacity volume of 595×10 6 m 3 and a catchment area of 1,584 km 2 ( [14]). The major tributaries that flow into Imha Reservoir are the Daegok Stream, the Banbyeon Stream, and the Yongjeon Stream ( [17]). Soil of the upstream basin of Imha Reservoir consists of fine reddish-brown clay sedimentary rocks that are prone to weathering.…”

Section: Study Areamentioning

confidence: 99%

The innovative operation of Imha Reservoir

Kim

Kim²,

Choi³

et al. 2022

E3S Web Conf.

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Imha Reservoir has both a water transfer tunnel connecting Andong Reservoir and a selective withdrawal facility (SWF), which enable the innovative operation of the reservoir. Although these facilities had not been equipped when constructed in 1993, these structural measures were applied afterward as needed for the effective reservoir operation. The tunnel was constructed in 2015 to minimize the spillway discharge of a reservoir during floods by moving water to the other reservoir with free space. The SWF was installed in 2006 to address problems due to the persistent turbidity of Imha Reservoir. The effectiveness of these facilities was demonstrated through the operational cases in October 2019, while some improvements to non-structural measures were derived simultaneously. To prevent damage to the fish ecosystem of Imha reservoir, the tunnel currently was operated in a one-way water movement. However, a two-way water movement should be allowed as designed in the mid to long term. The SWF was operated effectively based on the prediction of the fate and transport of turbid water inside the reservoir using CE-QUAL-W2. Nevertheless, more action procedures on turbid water are required, such as setting clear criteria for the release timing of turbid water, predicting the downstream turbidity variations, etc.

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A case study for ANN-based rainfall–runoff model considering antecedent soil moisture conditions in Imha Dam watershed, Korea

Cited by 7 publications

References 13 publications

Forecasting the Nysa Kłodzka flow rate in order to predict the available flow for a run-off-river (ROR) power plant

Forecasting the Nysa Kłodzka flow rate in order to predict the available flow for a run-off-river (ROR) power plant

A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer

The innovative operation of Imha Reservoir

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