• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.769-781
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• 2007

Factor analysis has been usually employed in reducing quantity of data and summarizing information on a system or phenomenon. In this analysis methodology, variables are grouped into several factors by consideration of statistic characteristics, and the results are used for dropping variables which have lower weight than others. In this study, factor analysis was applied for extracting primary factors influencing multi-dam system operation in the Han River basin, where there are two multi-purpose dams such as Soyanggang Dam and Chungju Dam, and water has been supplied by integrating two dams in water use season. In order to fulfill factor analysis, first the variables related to two dams operation were gathered and divided into five groups (Soyanggang Dam: inflow, hydropower product, storage management, storage, and operation results of the past; Chungju Dam: inflow, hydropower product, water demand, storage, and operation results of the past). And then, considering statistic properties, in the gathered variables, some variables were chosen and grouped into five factors; hydrological condition, dam operation of the past, dam operation at normal season, water demand, and downstream dam operation. In order to check the appropriateness and applicability of factors, a multiple regression equation was newly constructed using factors as description variables, and those factors were compared with terms of objective function used in operation water resources optimally in a river basin. Reviewing the results through two check processes, it was revealed that the suggested approach provided satisfactory results. And, it was expected for extracted primary factors to be useful for making dam operation schedule considering the future situation and previous results.

• Journal of the Korean Geophysical Society
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• v.7 no.3
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• pp.171-183
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• 2004

Electrical resistivity survey is widely used to investigate the stability of center-core type fill dam against the seepage phenomenon. In this study, we analyze the resistivity information obtained on a earth fill dam and compare it with the geotechnical SPT result. The analysis shows that the zones showing low resistivity value generally have low N value. However, some zones with high resistivity pattern do not accompany the increase of N value, and even showing low N value. These results imply that the direct identification of resistivity value to the real status of the core material of fill dam is impossible, and a highly resistive zone may be in serious status due to the effect increasing the resistivity value by the piping condition. Therefore, multiple exploration should be planned to reduce the uncertainty in application of geophysical methods to dam safety evaluation.

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

• Journal of the Korean GEO-environmental Society
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• v.11 no.9
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• pp.15-25
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• 2010

The main objective of this study was to offer informations about long-term seepage behavioral characteristics and to find a leakage safety management method for Juam Dam and Imha Dam, a central cored rockfill dams in Korea by the evaluating the automatically monitored leakage data. In the water leakage monitoring of fill dam, the generation of abnormal water leakage is difficult to directly detect due to the effect of outside factors such as the component of rainfall inherent in the observation value. Therefore, conventionally estimation methods of water leakage quantity were applied by multiple regression analysis considering reservoir water level, rainfall, etc.. However, the estimated error of rainfall component is relatively big in these method. This paper identifies the seepage characteristic of each dams which is not directly affected by rainfall through the hydrograph separation analysis and 3 dimensional analytical method, and thinks a leakage management method. It was noticed that two dams had site specific seepage behaviour features and were in stable state with the decreasing leakage quantity. It was also found that hydrograph separation method might be applicable to leakage safety management method.

• Earthquakes and Structures
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• v.25 no.2
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• pp.125-134
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• 2023

In the present work, a new global damage index is proposed for the seismic performance and failure analysis of concrete gravity dams. Unlike the existing indices of concrete structures, this index doesn't need scaling with an ultimate or an upper value. For this purpose, the Beni-Haroun dam in north-eastern Algeria, is considered as a case study, for which an average seismic capacity curve is first evaluated by performing several incremental dynamic analyses. The seismic performance point of the dam is then determined using the N2 method, considering multiple modes and taking into account the stiffness degradation. The seismic demand is obtained from the design spectrum of the Algerian seismic regulations. A series of recorded and artificial accelerograms are used as dynamic loads to evaluate the nonlinear responses of the dam. The nonlinear behaviour of the concrete mass is modelled by using continuum damage mechanics, where material damage is represented by a scalar field damage variable. This modelling, which is suitable for cyclic loading, uses only a single damage parameter to describe the stiffness degradation of the concrete. The hydrodynamic and the sediment pressures are included in the analyses. The obtained results show that the proposed damage index faithfully describes the successive brittle failures of the dam which increase with increasing applied ground accelerations. It is found that minor damage can occur for ground accelerations less than 0.3 g, and complete failure can be caused by accelerations greater than 0.45 g.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.159-167
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• 2004

We present a simple approach to finding the stationary workload of M/G/1 queues having generalized vacations and exhaustive service discipline. The approach is based on the level crossing technique. According to the approach, all that we need is the workload at the beginning of a busy period. An example system to which we apply the approach is the M/G/1 queue with both multiple vacations and D-policy.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Journal of Korea Water Resources Association
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• v.38 no.11
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• pp.937-946
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• 2005

The objective of the study is to develop a reservoir optimal operation model and to suggest the appropriate amount of additional water supply and optimal operation rule. The model uses multiple objective function and a global search method, SCE-UA method. The objective function is set up to maintain the storage at target level, to satisfy the water demand, and to maximize the hydropower product. To evaluate the model's applicability, the model was applied for allocating the optimal water depending on storage level changes of Seomjin dam. The results comparing optimal operation and historical data showed that hydropower product increased from $-2.29\%$ to $14.51\%$, $-5.94\%$ to $3.98\%$, and $-0.43\%$ to $6.35\%$ with varying target levels in wet, dry, and normal period, respectively. Also, The model was applied for assessing water supply capacity of Seomjin dam to satisfy increasing water demand. The dam was operated by the model on consideration of downstream flow as 0.17, 0.50, 0.70, 1.0, 1.5, and $3.0\;m^3/sec$. The results showed that in case of operating the dam with downstream flow less than $0.70\;m^3/sec$ and with target water level lower than 194.0 m, hydropower product was more than the historical operation data and existing amount of water supply was less influenced.

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.915-928
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• 2011

The main purpose of this study is to understand the effects of hydroclimatic factors on annual actual evapotranspiration and to suggest the multiple linear regression (MLR) equations for the estimation of annual actual evapotranspiration from watershed. To accomplish this study purpose, 5 dam watersheds (Goesan dam, Seomjingang dam, Soyanggang dam, Andong dam, Hapcheon dam) were selected as study watersheds and annual actual evapotranspiration was estimated based on annual water balance analysis from each watershed. The estimated annual actual evapotranspiration from water balance analysis was used to evaluate the MLR equations. Furthermore, the possibility of the estimation of actual evapotranspiration using potential evapotranspiration equations (Penman equation, FAO P-M equation, Makkink equation, Preistley-Taylor equation, Hargreaves equation) was evaluated. It has turned out that it is not appropriate to use potential evapotranspiration for the estimation of actual evapotranspiration because the correlation between actual evapotranspiration and potential evapotranspiration is very low. The comparison of MLR equations with current actual evapotranspiration equations indicates that MLR equations can be used for the estimation of annual actual evapotranspiration. Furthermore, it has turned out that the effects of hydroclimatic factors on annual actual evapotranspiration from dam watersheds are different in each watershed; however, for all watersheds in common precipitation has turned out to be the most important climatic factor affecting on the estimation of annual actual evapotranspiration.

• Water Engineering Research
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• v.3 no.2
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.