• 한국수자원학회논문집
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• 제41권9호
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• pp.907-917
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• 2008

본 연구에서는 PRMS, SLURP, SWAT 모형을 이용하여 유출모형에 따라 수자원의 기후변화 영향평가 결과에서 발생할 수 있는 차이를 평가하였다. 이를 위해 먼저 각 모형을 안동댐유역에 적용하여 관측자료에 대한 모의능력을 비교하였다. 그 다음 기온과 강수 변화를 가정한 합성시나리오 상황에서 각 모형별 모의결과를 비교하였다. 분석결과 세 모형은 관측기간에 대해서는 관측유량에 근접한 모의를 하였다. 그러나 강수와 기온의 변화를 고려하였을 경우에는 유출량의 변화량 모의에서 각 모형별로 상이한 결과를 보였다. 특히 기온이 크게 증가할 경우 모형별 결과차이가 증가하는 것으로 분석되었는데, 이것은 각 모델에서 이용하는 증발산량 추정방법의 차이가 가장 크게 영향을 미치는 것으로 분석되었다. 따라서 이러한 불확실성을 고려한 수자원 영향평가 방법이 필요할 것으로 판단되었다.

• Computers and Concrete
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• 제30권2호
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• pp.99-111
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• 2022

In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.

• 한국농공학회논문집
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• 제52권5호
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• pp.1-9
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• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.163-167
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• 2004

Creeks, defined by creek's improvement law, have strong localities in the flow characteristics and environmental condition. During the recent ten-years, lots of flood damages have occurred rather in the creeks. However, quantity and stream design information are poor while the national-class and local-class streams have sufficient. This causes a problem on improving the safety from flood. This study focuses on assessment of practical applicability for design flood estimation models. For this, Rational formula, Clark's model and Nakayath synthetic unit hydrograph method are estimated by data of the creek comprehensive improvement plan report, etc.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.

• 한국농공학회논문집
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• 제64권3호
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• 상하수도학회지
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• 제34권6호
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• pp.503-512
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• 2020

Identifying the water circulation status is one of the indispensable processes for watershed management in an urban area. Recently, various water circulation models have been developed to simulate the water circulation, but it takes a lot of time and cost to make a water circulation model that could adapt the characteristics of the watershed. This paper aims to develop a water circulation state estimation model that could easily calculate the status of water circulation in an urban watershed by using multiple linear regression analysis. The study watershed is a watershed in Seoul that applied the impermeable area ratio in 1962 and 2000. And, It was divided into 73 watersheds in order to consider changes in water circulation status according to the urban characteristic factors. The input data of the SHER(Similar Hydrologic Element Response) model, a water circulation model, were used as data for the urban characteristic factors of each watershed. A total of seven factors were considered as urban characteristic factors. Those factors included annual precipitation, watershed area, average land-surface slope, impervious surface ratio, coefficient of saturated permeability, hydraulic gradient of groundwater surface, and length of contact line with downstream block. With significance probabilities (or p-values) of 0.05 and below, all five models showed significant results in estimating the water circulation status such as the surface runoff rate and the evapotranspiration rate. The model that was applied all seven urban characteristics factors, can calculate the most similar results such as the existing water circulation model. The water circulation estimation model developed in this study is not only useful to simply estimate the water circulation status of ungauged watersheds but can also provide data for parameter calibration and validation.

• 한국수자원학회논문집
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• 제55권3호
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• pp.229-243
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• 2022

우리나라 11개 기상관측지역의 월별 기상자료가 증발접시계수에 미치는 영향을 분석하고, 증발접시계수 산정을 위한 4가지 형태의 다변량 선형회귀모형의 적용성을 검토하였다. 개발된 증발접시계수 산정모형의 적용성을 평가하기 위해서 기존에 다른 연구자들에 의해서 제안된 6가지의 모형과 비교 평가하였다. 우리나라 11개 기상관측지역에서 증발접시계수는 1, 2, 3, 7, 11, 12월은 기온에 가장 큰 영향을 받고, 다른 월들은 일사량에 가장 큰 영향을 받는 것으로 나타났다. 전반적으로 모든 월에서 풍속과 상대습도는 기온이나 일사량과 비교해서 증발접시계수에 큰 영향을 미치지 않는 것으로 나타났다. 모든 지역과 월에서 각 지역별로 5개의 독립변수(풍속, 상대습도, 기온, 일조시간과 가조시간의 비, 일사량)를 적용하여 유도된 모형이 가장 양호한 증발량 산정 결과를 보였다. 모형 검증결과에 의하면 다변량 선형회귀분석을 적용하여 증발접시계수를 산정하는 경우 일부 지역과 월에서 제한적으로 적용할 수 있을 것으로 판단된다.

• 한국농공학회논문집
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• 제57권2호
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• pp.37-45
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• 2015

Typically, water quality sampling takes place intermittently since sample collection and following analysis requires substantial cost and efforts. Therefore regression models (or rating curves) are often used to interpolate water quality data. LOADEST has nine regression models to estimate water quality data, and one regression model needs to be selected automatically or manually. The nine regression models in LOADEST and auto-selection by LOADEST were evaluated in the study. Suspended solids data were collected from forty-nine stations from the Water Information System of the Ministry of Environment. Suspended solid data from each station was divided into two groups for calibration and validation. Nash-Stucliffe efficiency (NSE) and coefficient of determination ($R_2$) were used to evaluate estimated suspended solid loads. The regression models numbered 1 and 3 in LOADEST provided higher NSE and $R_2$, compared to the other regression models. The regression modes numbered 2, 5, 6, 8, and 9 in LOADEST provided low NSE. In addition, the regression model selected by LOADEST did not necessarily provide better suspended solid estimations than the other regression models did.

• 한국지반환경공학회 논문집
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• 제9권7호
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• pp.73-85
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• 2008

우리나라에는 1995년부터 전국에 지하수 관측소를 설치하여 2005년에 320개소를 완료하였으며, 일 4회 지하수위 자료가 자동 측정되고 있다. 지하수 수위 강하곡선법으로 산정한 관측 지점에서의 지하수 함양율 자료의 평균값을 유역 평균 함양율로 사용하는 것은 대표성이 결여되어 있기 때문에 한계가 있다. 따라서, 본 연구에서는 지하수위 미계측 지역을 대상으로 지하수 함양율을 추정할 수 있도록 223개 관측 지점의 특성 인자와 지하수 함양율과의 관계를 통계적 기법을 활용하여 분석하고 이를 토대로 회귀모형을 구축하였다. 본 연구에서는 군집분석을 통하여 분석대상 관측정을 선정하고, 분산분석을 통하여 지하수 함양량 추정에 필요한 4가지 인자(대상 지점 인근하천의 규모, 하천까지 거리, 지형 경사도, 암석 성인)를 추출하였으며, 이들 인자에 대한 각 관측지점의 특성 자료를 수집하여 회귀 모형에 적합시킨 결과 미계측 지역의 지하수 함양율 추정이 가능한 것으로 평가되었다.