• 한국관개배수논문집
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• 제18권2호
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• pp.66-75
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• 2011

Multivariate statistical analysis was applied to 110 dam heightening reservoir to classify the building conditions for waterfront centered around cultivated area using data of land cover, landscape, additional water quantity, local economic, tourism resources, and accessibility related variables. Five factors were extracted through factor analysis based on eigen value criteria of more than one. These five factors together account for 68.2% of the total variance. Characteristics of five factors for the downstream of dam heightening reservoirs are building conditions of waterfront, economic conditions, additional water quantity, eco-tours, and accessibility of tourism resources respectively. Five clusters were classified through cluster analysis based on factor score. The classified result shows that third cluster has remunerative terms for building waterfront.

• 한국농공학회논문집
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• 제61권3호
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• pp.101-112
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• 2019

This study developed selection criteria of small-scales reservoirs, having under $300,000m^3$ storage capacity, for the Emergency Action Plan(EAP) establishment in order to reduce the disaster risks of the reservoir's failures. Those reservoirs are out of ranges of Korean EAP establishment standard, but have potential risk of disasters as they have often failed by the recent extreme rainfall events and earthquakes, causing economical and life losses. The problem of reservoir aging is also one of the reasons of them. In this study, the developed selection criteria of small reservoirs for EAP establishment are storage capacity, embankment height, reservoir age, heavy rain factor and earthquake factor. These criteria were selected based on the review of the existing EAP establishment guidelines, analysis of the past dam failure cases, and the previous related studies. The quantification of these criteria were conducted for the practical applications in the fields, and applied to 67 previous failures in order to investigate the relation of each criteria with these failures. The earthquake factor found to be the highest relations followed by heavy rain factors, combination of earthquake and heavy rain factors, and reservoir age. The classification was made as observation and review groups for EAP establishments based on overlapping numbers of each criteria. This classifications applied to 354 reservoirs designated as having the potential disaster risk by MOIS, and showed 38.4% of observation and 11.9% of review groups. Anticipatory monitoring and regular inspection should be made by professional facility managers for the observation group, and necessity of EAP establishment should be assessed for the review group based on the downstream status and financial budget.

• 한국농공학회논문집
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• 제47권7호
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• pp.13-22
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• 2005

Main purpose of this article is to classify reservoirs corresponding to their physical characteristics, for example, dam height, dam width, age, repair-works history. First of all, data set of 13,976 reservoirs was analyzed using k means and self organized maps. As a result of these analysis, lots of reservoirs have been classified into four clusters. Factors and their critical values to classify the reservoirs into four groups have been founded by generating a decision tree. The path rules to each group seem reasonable since their survivor function showed unique pattern.

• 생태와환경
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• 제46권3호
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• pp.343-359
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• 2013

우리나라 저수지에서 호안 환경의 특성을 파악하기 위하여, 수위변동폭과 이용 목적에 따라서 35개 저수지를 선정하여 지형, 수문, 수질 및 토양 환경요인을 조사하여 이들 사이의 관계를 분석하고 환경 특성에 따라 저수지의 유형을 구분하였다. 저수지의 지형적, 수문적 특성은 그 조성된 곳의 환경과 크기에 따라서 다양하였다. 저수지의 연수위변동폭은 1~27 m로서 변이가 컸다. 저수지의 수질은 대부분 중영양 혹은 부영양 상태이었다. 호안의 토양 환경은 모래 함량이 많았다. 저수지의 수위변동폭, 빈도 및 기간은 저수지의 이용 목적에 따라서 독특한 특성을 보였다. 홍수조절용 저수지는 수위변동 폭이 크고 빈도는 낮았으며 수력발전용 저수지는 수위변동 폭이 크고 빈도가 높았다. 회귀분류 나무(CART) 분석 결과에 의하면 저수지의 수질은 수심, 수위변동폭 및 고도에 의하여 구분되었다. 주요인분석(PCA)의 결과에 의하면 환경요인에 의하면 저수지의 유형은 저수지의 크기, 수위변동폭, 수질 및 토양의 토성과 유기물 함량에 의하여 구분되었다. 이상의 결과를 종합하면, 우리나라 저수지의 호안에서 저수지의 크기, 수위변동폭, 수질 및 호안 토양의 특성이 중요한 환경요인인 것으로 판단되었다.

• 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.

• Advances in Energy Research
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• 제6권2호
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• pp.103-129
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• 2019

While global demand for energy increases annually, at the same time the demand for carbon-free, sulphur-free and NO_x-free energy sources grows considerably. This state poses a challenge in the research for newer sources like biomass and shale gas as well as renewable energy resources such as solar, wind, geothermal and hydraulic energy. Although wave energy also is a form of renewable energy it has not fully been exploited technically and economically so far. This study tries to explain those reasons in which it is beyond doubt that the demand for wave energy will soon increase as fossil energy resources are depleted and environmental concerns gain more importance. The electrical energy supplied to the grid shall be produced from wave energy whose conversion devices can basically work according to three different systems. i. Systems that exploit the motions or shape deformations of their mechanisms involved, being driven by the energy of passing waves. ii. Systems that exploit the weight of the seawater stored in a reservoir or the changes of water pressure by the oscillations of wave height, iii. Systems that convert the wave motions into air flow. One of the aims of this study is to present the classification deficits of the wave energy converters (WECs) of the "wave developers" prepared by the European Marine Energy Center, which were to be reclassified. Furthermore, a new classification of all WECs listed by the European Marine Energy Center was arranged independently. The other aim of the study is to assess the technological state of the art of these WECs designed and/or produced, to obtain an overview on them.

• 생태와환경
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• 제41권spc호
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• pp.1-10
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• 2008

We patterned water quality of agricultural reservoirs according to the differences of six physico-chemical environmental factors (TN, TP, DO, BOD, COD, and SS) using four different ordination methods: Principal Components Analysis (PCA), Detrended Correspondence Analysis (DCA), Nonmetric Multidimensional Scaling (NMS), and Isometric Feature Mapping (Isomap). The data set was obtained from the water quality monitoring networks operated by the Ministry of Agriculture and Forestry and the Ministry of Environments. Chlorophyll-${\alpha}$ displayed the highest correlation with COD, followed by TP, BOD, SS, and TN (p<0.01), while negatively correlated with altitude and bank height of the reservoirs (p<0.01). Although four different ordination methods similarly patterned the reservoirs according to the gradient of nutrient concentration, PCA and NMS appeared to be the most efficient methods to pattern water quality of reservoirs based on the explanation power. Considering variable scores in the ordination map, the concentration of nutrients was positively correlated with Chl-${\alpha}$, while negatively correlated with altitude and bank height. These ordination methods may help to pattern agricultural reservoirs according to their water quality characteristics.

• 한국농공학회논문집
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• 제65권1호
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• pp.15-26
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• 2023

This study aimed to evaluate the performance of water level classification from CCTV images in agricultural facilities such as reservoirs. Recently, the CCTV system, widely used for facility monitor or disaster detection, can automatically detect and identify people and objects from the images by developing new technologies such as a deep learning system. Accordingly, we applied the ResNet-50 deep learning system based on Convolutional Neural Network and analyzed the water level of the agricultural reservoir from CCTV images obtained from TOMS (Total Operation Management System) of the Korea Rural Community Corporation. As a result, the accuracy of water level detection was improved by excluding night and rainfall CCTV images and applying measures. For example, the error rate significantly decreased from 24.39 % to 1.43 % in the Bakseok reservoir. We believe that the utilization of CCTVs should be further improved when calculating the amount of water supply and establishing a supply plan according to the integrated water management policy.

• 대한토목학회논문집
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• 제41권4호
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• pp.449-459
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• 2021

농업용 저수지의 80 % 이상이 50년 이상 된 노후 시설물로 안전성 및 기능 저하가 발생하고 있다. 이로 인해 저수지의 붕괴 등 안전사고가 발생하고 있는 실정이다. 이에 따라서 저수지의 붕괴 등의 안전사고를 미연에 방지하고자 정밀안전진단을 실시하고 우선순위에 따라 수리시설개보수사업을 시행하고 있다. 하지만 사업 우선순위 선정의 대부분은 시설물 관리자의 주관적인 판단을 통해 이루어지고 있다. 이에 본 연구에서는 정밀안전진단 결과 및 기존의 수리시설 개보수사업의 의사결정 사례를 D/B화하여 80개의 가설을 설정하고 상관분석 및 유의성검정을 통해 45개의 변수를 선정하였다. 선정된 변수들을 로지스틱회귀분석을 이용하여 의사결정지원모델을 제시하였다. 의사결정지원모델의 변수는 총 21개가 채택되었으며 모델의 분류 정확도는 86.8 %로 나타났다. 본 연구는 수리시설개보수사업 선정을 위한 의사결정의 정량적인 지표를 제시 한 부분에 중요한 의의를 가진다.

• 대한원격탐사학회지
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• 제38권6_3호
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• pp.1777-1788
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• 2022

저수지는 국내 영농환경에서 주요한 용수 공급처이며, 저수지의 저수량 파악은 농업용수의 활용 및 관리차원에서 중요하다. 위성영상을 활용한 원격탐사는 저수지와 같이 광역적으로 분포하는 객체에 대하여 정기적인 모니터링을 할 수 있는 효과적인 수단으로, 본 연구에서는 Sentinel-1 Synthetic Aperture Radar (SAR) 영상을 통해 영상분류 및 영상분할 알고리즘을 적용하여 국내 저수지 53개소의 수표면적 탐지를 수행하였다. 사용한 알고리즘은 Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Otsu, Watershed (WS), Chan-Vese (CV)로 총 6가지이며, 드론으로 촬영한 실측 정사영상으로 수표면적 탐지 결과를 평가하였다. 각 알고리즘으로부터 산출된 수표면적과 실측 수표면적간의 상관성은 NN 0.9941, SVM 0.9942, RF 0.9940, Otsu 0.9922, WS 0.9709, CV 0.9736로 나타났으며, 저수지 유효저수량의 규모가 클수록 더 높은 선형 상관관계를 보였다. 혼동 행렬로부터 산출한 정확도, 정밀도, 재현율을 통해 알고리즘간 수표면적 탐지 정확도와 탐지 경향을 분석하였다. 정확도의 경우 각 10만 m³ 미만 저수지에서 WS가 0.8752, 10만~30만 m³에서 Otsu가 0.8845, 30만~50만 m³에서 RF가 0.9100, 50만 m³ 이상에서 Otsu와 CV가 0.9400으로 가장 높은 수치를 보였다. WS의 경우 수표면적을 미탐지하는 경향으로 인해 낮은 재현율을 보였고, NN, SVM, RF의 경우 과대 탐지로 인한 낮은 정밀도를 보였다. SAR 영상을 통한 수표면적 탐지 시 저수지 수표면의 수생식물 및 인공건축물이 미탐지를 발생시키는 오차 요소로 작용함을 분석결과 및 실측영상을 통해 확인하였다.