• KCID journal
• /
• v.18 no.2
• /
• pp.66-75
• /
• 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.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.3
• /
• pp.101-112
• /
• 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.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.47 no.7
• /
• pp.13-22
• /
• 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.

• Korean Journal of Ecology and Environment
• /
• v.46 no.3
• /
• pp.343-359
• /
• 2013

In order to understand shoreline environment characteristics of Korean reservoirs, the interrelationships between environmental factors of geomorphology, hydrology, water quality and shoreline soil were analyzed, and the reservoir types were classified according to their environmental characteristics in the 35 reservoirs selected by considering the purpose of dam operations and annual water-level fluctuations. Geomorphological and hydrological characteristics of reservoirs were correlated with the altitude and the size scale of reservoirs. The annual range of water level fluctuation showed a wide variation from 1 m to 27 m in the various reservoirs in Korea. The levels of eutrophication of most reservoirs were mesotrophic or eutrophic. From the result of the soil texture analysis, sand contents were high in reservoir shorelines. Range, frequency and duration of water-level fluctuation were distinctive from the primary function of reservoirs. Flood control reservoirs had a wide range with low frequency and waterpower generation reservoirs had a narrow range with high frequency in the water-level fluctuation. According to the result of CART (classification and regression tree) analysis, the water quality of reservoirs was classified by water depth, range of water-level fluctuation and altitude. The result of PCA (principal component analysis) showed that the type of reservoirs was classified by reservoir size, water-level fluctuation, water quality, soil texture and soil organic matter. In conclusion, reservoir size, the water-level fluctuation, water quality and soil characteristics might be major factors in the environment of reservoir shorelines in Korea.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.3
• /
• pp.63-73
• /
• 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
• /
• v.6 no.2
• /
• pp.103-129
• /
• 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.

• Korean Journal of Ecology and Environment
• /
• v.41 no.spc
• /
• pp.1-10
• /
• 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.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.65 no.1
• /
• pp.15-26
• /
• 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.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.41 no.4
• /
• pp.449-459
• /
• 2021

More than 80 % of agricultural reservoirs are old facilities over 50 years old, and safety and function declines occur. As a result, safety accidents such as the collapse of the reservoir have occurred. Precise safety diagnosis is conducted in advance to prevent accidents such as reservoir collapse, and Water resources facility improvement project are implemented based on priority. However, the priority of the business is selected based on the subjective judgment of the facility manager. In this study, we set 80 hypotheses based on the results of precision safety diagnosis and decision-making examples of existing Water resources facility improvement project and selected 45 variables using correlation analysis and significance test. Using logistic regression analysis, the final 21 variables were selected and a decision support model was presented, and the classification accuracy of the model was 86.8 %. In this research, the part that presented the quantitative index for decision support when selecting the Water resources facility improvement project has important significance.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_3
• /
• pp.1777-1788
• /
• 2022

The reservoir is a major water supply source in the domestic agricultural environment, and the monitoring of water storage of reservoirs is important for the utilization and management of agricultural water resource. Remote sensing via satellite imagery can be an effective method for regular monitoring of widely distributed objects such as reservoirs, and in this study, image classification and image segmentation algorithms are applied to Sentinel-1 Synthetic Aperture Radar (SAR) imagery for water body detection in 53 reservoirs in South Korea. Six algorithms are used: Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Otsu, Watershed (WS), and Chan-Vese (CV), and the results of water body detection are evaluated with in-situ images taken by drones. The correlations between the in-situ water surface area and detected water surface area from each algorithm are NN 0.9941, SVM 0.9942, RF 0.9940, Otsu 0.9922, WS 0.9709, and CV 0.9736, and the larger the scale of reservoir, the higher the linear correlation was. WS showed low recall due to the undetected water bodies, and NN, SVM, and RF showed low precision due to over-detection. For water body detection through SAR imagery, we found that aquatic plants and artificial structures can be the error factors causing undetection of water body.