• Geomechanics and Engineering
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• v.14 no.3
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• pp.241-246
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• 2018

The displacement monitoring data series reconstruction method was developed under equal water level effects based on displacement monitoring data of concrete dams. A dam displacement variation equation was set up under the action of temperature and aging factors by optimized analysis techniques and then the dam displacement hydraulic pressure components can be separated. Through the dynamic adjustment of temperature and aging effect factors, the aging component isolation method of dam displacement was developed. Utilizing the isolated dam displacement aging components, the dam stability model was established. Then, the dam stability criterion was put forward based on convergence and divergence of dam displacement aging components and catastrophe theory. The validity of the proposed method was finally verified combined with the case study.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.788-791
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• 2008

On this study, following works have been carried out : division of Han River Basin into 24 sub basins, use of rainfall data of 151 stations to make spatial distribution of rainfall, selection of control points such as Soyanggang Dam, Chungju Dam, Chungju Release Control Dam, Heongseong Dam, Hwachun Dam, Chuncheon Dam, Uiam Dam, Cheongpyung Dam and Paldang Dam, selection of SSARR (Streamflow Synthesis and Reservoir Regulation) model as a hydrologic model, preparation of input data of SSARR model, sensitivity analysis of parameter using hydrologic data of 2002. The sensitivity analysis showed that soil moisture index versus runoff percent (SMI-ROP), baseflow infiltration index versus baseflow percent (BII-BFP) and surface-subsurface separation (S-SS) parameters are higher sensitive parameters to the simulation result.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.437-437
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• 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
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• v.53 no.1
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• pp.71-82
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• 2020

The objective of this study is to evaluate the instream flow in the North Han River basin according to the operation of Imnam Dam in North Korea. The water budget and instream flow satisfaction were analyzed using hourly, daily and monthly data of Water Management Information System (WAMIS) from Jan. 1991 to Dec. 2018. As a analysis result of water budget using hourly data in the North Han River basin, although inflows compared with dam release in the upstream basin of Peace Dam-Hwacheon Dam and Chuncheon Dam-Soyanggang Dam-Uiam Dam were calculated as negative values, the reasonable results using daily and monthly average data were estimated. It showed that the results of water budget analysis of dam inflow and total release may be different by time units of data. The monthly average inflow of Hwacheon Dam decreased significantly after the construction in 2003 of Imnam Dam, which confirmed that the operation of Imnam Dam had a significant effect on the dams in the North Han River basin. The operation of Imnam Dam is one of the main reasons for the lack of instream flow and total shortage amounts and shortage period increased up to +330% due to the decrease in inflow and total release of dams in the North Han River water after the operation of Imnam Dam. It is necessary to study various plans to secure instream flow including transboundary river management

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.2
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• pp.63-74
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• 2019

The objective of this study was to evaluate the influence of rainfall observation network on daily dam inflow using artificial neural networks(ANNs). Chungju Dam and Soyangriver Dam were selected for the study watershed. Rainfall and dam inflow data were collected as input data for construction of ANNs models. Five ANNs models, represented by Model 1 (In watershed, point rainfall), Model 2 (All in the Thiessen network, point rainfall), Model 3 (Out of watershed in the Thiessen network, point rainfall), Model 1-T (In watershed, area mean rainfall), Model 2-T (All in the Thiessen network, area mean rainfall), were adopted to evaluate the influence of rainfall observation network. As a result of the study, the models that used all station in the Thiessen network performed better than the models that used station only in the watershed or out of the watershed. The models that used point rainfall data performed better than the models that used area mean rainfall. Model 2 achieved the highest level of performance. The model performance for the ANNs model 2 in Chungju dam resulted in the $R^2$ value of 0.94, NSE of 0.94 $NSE_{ln}$ of 0.88 and PBIAS of -0.04 respectively. The model-2 predictions of Soyangriver Dam with the $R^2$ and NSE values greater than 0.94 were reasonably well agreed with the observations. The results of this study are expected to be used as a reference for rainfall data utilization in forecasting dam inflow using artificial neural networks.

• Computers and Concrete
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• v.18 no.6
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• pp.1153-1173
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• 2016

According to deformation data measured in some high concrete dams, for dam body deformation, there is a complex relationship with dam height and water head for different projects, instead of a simple monotonic relationship consistently. Meanwhile, settlement data of some large reservoirs exhibit a significant deformation of reservoir basin. As water conservancy project with high concrete dam and large storage capacity increase rapidly these decades, reservoir basin deformation problem has gradually gained engineers' attentions. In this paper, based on conventional analytical method, an improved analytical method for high concrete dam is proposed including the effect of reservoir basin deformation. Though establishing FEM models of two different scales covering reservoir basin and near dam area respectively, influence of reservoir basin on dam body is simulated. Then, forward and inverse analyses of concrete dam are separately conducted with conventional and proposed analytical methods. And the influence of reservoir basin deformation on dam working behavior is evaluated. The results of two typical projects demonstrate that reservoir basin deformation will affect dam deformation and stress to a certain extent. And for project with large and centralized water capacity ahead of dam site, the effect is more significant than those with a slim-type reservoir. As a result, influence of reservoir basin should be taken into consideration with conducting analysis of high concrete dam with large storage capacity.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.361-364
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• 2005

The use of remotely sensed data and geographic information system (GIS) to develop conservation-oriented watershed management strategies on Imha Dam, Korea, is presented. The change of land use for study area was analyzed using multi-temporal Landsat imagery. A soil loss model was executed within a GIS environment to evaluate watershed management strategies in terms of soil loss. In general, remotely sensed data provide efficient means of generating the input data required for the soil loss model. Also, GIS allowed for easy assessment of the relative erosion hazard over the watershed under the different land use change options. The soil loss model predicted substantial declines in soil loss under conservation-oriented land management compared to current land management for Imha Dam. The results of this study indicate that soil loss potential (5,782,829 ton/yr) on Imha Dam in 2003 is approximately 1.27 times higher than that (4,557,151 ton/yr) in 1989. This study represents the first attempt in the application of GIS technology to watershed conservation planning for Imha Dam. The procedures developed will contribute to the evolution of a decision support system to guide the land planning and dam management in Imha Dam.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1596-1601
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• 2005

Recently, the probable maximum precipitation (PMP) of dam sites has been greatly increased, compared to that in design, due to a rise in precipitation by abnormal weather, which led to an increase in National interest for dam safety. Therefore, the purpose of this study is to develop a management system of dam safety. The main contents of the first stage($'03{\sim}'04$) of the project consisted of determining the object of management system of dam safety through researched present situation of dam safety management in domestic and reviewing operation for management system of dam safety in abroad. In the second stage($'05{\sim}'06$), the study pursues constructing a basis process of synthetic safety management system through dam safety program and developing a system that can judge dam safety with an improve in reliability of measurement data.