• Journal of Korea Water Resources Association
• /
• v.52 no.7
• /
• pp.451-461
• /
• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• Journal of Korea Water Resources Association
• /
• v.51 no.12
• /
• pp.1217-1227
• /
• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2002.05a
• /
• pp.59-64
• /
• 2002

An expert system, to assist dam managers for five dams along the Saikawa River, has been developed with a primary objective of achieving swift and accurate reservoir operation decision-makings during floods. The expert system is capable of supporting on decision-makings upon establishment of flood management procedure and release/storage planning. Furthermore, an attempt was made to improve reservoir inflow prediction models for better supporting capability. As a result, accuracy on prediction of inflow up to 7 hours ahead was improved, which is important for flood management of the five dams, using neural network. The neural network inflow prediction models were developed for each types of floods caused by frontal rainfalls, snowmelt and typhoons, after extracting relevant meteorological factors for each.

• Geomechanics and Engineering
• /
• v.38 no.3
• /
• pp.215-229
• /
• 2024

Because of the various patterns of deep-water inrush and complicated mechanisms, accurately predicting mine water inflows is always a difficult problem for coal mine geologists. In study presented in this paper, the water inrush channels were divided into four basic water diversion structures: aquifer, rock fracture zone, fracture zone and goaf. The fluid flow characteristics in each water-conducting structure were investigated by laboratory tests, and multistructure and multisystem coupling flow analysis models of different water-conducting structures were established to describe the entire water inrush process. Based on the research of the water inrush flow paths, the analysis model of different water inrush space structures was established and applied to the prediction of mine water inrush inflow. The results prove that the conduction sequence of different water-conducting structures and the changing rule of permeability caused by stress changes before and after the peak have important influences on the characteristics of mine water-gushing. Influenced by the differences in geological structure and combined with rock mass RQD and fault conductivity characteristics and other mine exploration data, the prediction of mine water inflow can be realized accurately. Taking the water transmitting path in the multistructure as the research object of water inrush, breaking through the limitation of traditional stratigraphic structure division, the prediction of water inflow and the estimation of potentially flooded area was realized, and water bursting intensity was predicted. It is of great significance in making reasonable emergency plans.

• Journal of Korea Water Resources Association
• /
• v.50 no.7
• /
• pp.489-502
• /
• 2017

The objective of this study is to propose and evaluate the BAYES-ESP, which is a dam inflow prediction method based on Ensemble Streamflow Prediction method (ESP) and Bayesian theory. ABCD rainfall-runoff model was used to predict monthly dam inflow. Monthly meteorological data collected from KMA, MOLIT and K-water and dam inflow data collected from K-water were used for the model calibration and verification. To estimate the performance of ABCD model, ESP and BAYES-ESP method, time series analysis and skill score (SS) during 1986~2015 were used. In time series analysis monthly ESP dam inflow prediction values were nearly similar for every years, particularly less accurate in wet and dry years. The proposed BAYES-ESP improved the performance of ESP, especially in wet year. The SS was used for quantitative analysis of monthly mean of observed dam inflows, predicted values from ESP and BAYES-ESP. The results indicated that the SS values of ESP were relatively high in January, February and March but negative values in the other months. It also showed that the BAYES-ESP improved ESP when the values from ESP and observation have a relatively apparent linear relationship. We concluded that the existing ESP method has a limitation to predict dam inflow in Korea due to the seasonality of precipitation pattern and the proposed BAYES-ESP is meaningful for improving dam inflow prediction accuracy of ESP.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.437-437
• /
• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• Journal of Korea Water Resources Association
• /
• v.49 no.5
• /
• pp.361-372
• /
• 2016

Reliable long-term dam inflow prediction is necessary for efficient multi-purpose dam operation in changing climate. Since 2000s the teleconnection between global climate indices (e.g., ENSO) and local hydroclimate regimes have been widely recognized throughout the world. To date many hydrologists focus on predicting future hydrologic conditions using lag teleconnection between streamflow and climate indices. This study investigated the utility of teleconneciton for predicting dam inflow with 1-month lead time at Andong dam basin. To this end 40 global climate indices from NOAA were employed to identify potential predictors of dam inflow, areal averaged precipitation, temperature of Andong dam basin. This study compared three different approaches; 1) dam inflow prediction using SWAT model based on teleconneciton-based precipitation and temperature forecast (SWAT-Forecasted), 2) dam inflow prediction using teleconneciton between dam inflow and climate indices (CIR-Forecasted), and 3) dam inflow prediction based on the rank of current observation in the historical dam inflow (Rank-Observed). Our results demonstrated that CIR-Forecasted showed better predictability than the other approaches, except in December. This is because uncertainties attributed to temporal downscaling from monthly to daily for precipitation and temperature forecasts and hydrologic modeling using SWAT can be ignored from dam inflow forecast through CIR-Forecasted approach. This study indicates that 1-month lead dam inflow forecast based on teleconneciton could provide useful information on Andong dam operation.

• Journal of Korea Water Resources Association
• /
• v.51 no.7
• /
• pp.607-616
• /
• 2018

Flow regime-based ANFIS Dam Inflow Prediction (FADIP) model is developed and compared with ANFIS Dam Inflow Prediction (ADIP) model in this study. The selected study area is the Chungju and Soyang multi-purpose dam watersheds in South Korea. The dam inflow, precipitation and monthly weather forecast information are used as input variables of the models. The training and validation periods of the models are 1987~2010 for Chungju and 1984~2010 for Soyang dam watershed. The testing periods for both watersheds are 2011~2016. The results of training and validation indicate that FADIP has better training ability than ADIP for predicting dam inflow in normal and low flow regimes. In the result of testing, ADIP shows low predictability of dam inflow in the low flow regime due to the model tuning on all flow regime together. However, FADIP demonstrates the improved accuracy over the entire period compared to ADIP, especially during the normal and low flow seasons. It is concluded that FADIP is valuable for the prediction of dam inflow in the case of drought years, and useful for water supply management of the multi-purpose dam.

• Journal of Environmental Impact Assessment
• /
• v.20 no.4
• /
• pp.435-442
• /
• 2011

In this study, a regression model was developed for prediction of inflow temperature to support an effective thermal stratification simulation of Yongdam Reservoir, using the relationship between gaged inflow temperature and air temperature. The effect of reproductability for thermal stratification was evaluated using EFDC model by gaged vertical profile data of water temperature(from June to December in 2005) and ex-developed regression models. Therefore, in the development process, the coefficient of correlation and determination are 0.96 and 0.922, respectively. Moreover, the developed model showed good performance in reproducing the reservoir thermal stratification. Results of this research can be a role to provide a base for building of prediction model for water quality management in near future.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2002.03a
• /
• pp.181-188
• /
• 2002

Water flow rate into the tunnel usually determined by numerical analyses and mathematical formulas using water levels and permeability is obtained only a few limited districts of the whole tunneling site. However, underground is not a homogeneous but complicated mass. Therefore these methods can't reflect structural and geological aspects. In this study, assuming that the mountain stream in droughty season is to be the same as baseflow of its basin, hydrological method is applied to predict the constant water flow rate into the tunnel on construction field. Prediction of constant water inflow rate is performed on each section of tunnel construction field divided into 20 sections.