• KCID journal
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• v.17 no.2
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.433-442
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• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.197-205
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• 2004

Wetlands are lands with a mixture of water, herbaceous or woody vegetation and wet soil. And linear spectral mixture analysis (LSMA) is one of the most often used methods in handling the spectral mixture problem. This study aims to test LSMA is an enhanced routine for classification of wetland land-covers in Paldang reservoir and vicinity (paldang Reservoir) using Landsat TM and ETM+ imagery. In the LSMA process, reference endmembers were driven from scatter-plots of Landsat bands 3, 4 and 5, and a series of endmember models were developed based on green vegetation (GV), soil and water endmembers which are the main indicators of wetlands. To consider phenological characteristics of Paldang Reservoir, a soil endmember was subdivided into bright and dark soil endmembers in spring and a green vegetation (GV) endmember was subdivided into GV tree and GV herbaceous endmembers in fall. We found that LSMA fractions improved the classification accuracy of the wetland land-cover. Four endmember models provided better GV and soil discrimination and the root mean squared (RMS) errors were 0.011 and 0.0039, in spring and fall respectively. Phenologically, a fall image is more appropriate to classify wetland land-cover than spring's. The classification result using 4 endmember fractions of a fall image reached 85.2 and 74.2 percent of the producer's and user's accuracy respectively. This study shows that this routine will be an useful tool for identifying and monitoring the status of wetlands in Paldang Reservoir.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.1-8
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• 2009

With the development of IT convergence technology and the construction of master plan for the four rivers restoration of the government, the importance of the eco-friendly water pollution management is being spotlighted. In this paper, we proposed the effective water pollution management using the reservoir tank automatic classification for improving the water quality and on-line managing efforts of ceo-friendly reservoir tanks. The proposed method defined the seven factors of water pollution evaluation and managed the water pollution according to hydrogen ion concentration(pH), chemical oxygen demand(COD), suspend solid(SS), dissolved oxygen(DO), count of coliform group(MPN), total phosphorus(T-P), and total nitrogen(T-N) using the sensors. We measured the values for the seven factors from the reservoir tank and normalized to ranging from 1 to 9. To evaluate the performance of the water pollution management using the reservoir tank automatic classification, we conducted F-measure so as to verify usefulness. This evaluation found that the difference of satisfaction by the traditional system was statistically meaningful.

• Journal of Korean Society on Water Environment
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• v.27 no.6
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• pp.794-803
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• 2011

Self Organizing Map (SOM) was applied for pattern classification of hydrological and water quality data measured at Jangseong Reservoir on a monthly basis. The primary objective of the present study is to understand better data characteristics and relationship between the data. For the purpose, two SOMs were configured by a methodologically systematic approach with appropriate methods for data transformation, determination of map size and side lengths of the map. The SOMs constructed at the respective measurement stations for water quality data (JSD1 and JSD2) commonly classified the respective datasets into five clusters by Davies-Bouldin Index (DBI). The trained SOMs were fine-tuned by Ward's method of a hierarchical cluster analysis. On the one hand, the patterns with high values of standardized reference vectors for hydrological variables revealed the high possibility of eutrophication by TN or TP in the reservoir, in general. On the other hand, the clusters with low values of standardized reference vectors for hydrological variables showed the patterns with high COD concentration. In particular, Clsuter1 at JSD1 and Cluster5 at JSD2 represented the worst condition of water quality with high reference vectors for rainfall and storage in the reservoir. Consequently, SOM is applicable to identify the patterns of potential eutrophication in reservoirs according to the better understanding of data characteristics and their relationship.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.839-852
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• 2018

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

• Water for future
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• v.10 no.1
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• pp.95-100
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• 1977

The empirical area-reduction method by W.M. Borland and C.R. Miller and its revised procedure by W.T. Moody were made of fitting the area and storage curves to the Van't Hul distributions. It should be noted that the reservoir is classified into one of the four standard types on the basis of the topographical feature of the reservoir in application of the method. In other words, this method did not take into account several considerafble factors affecting the mode of sediment deposition, but only the shape of the reservoir as a governign factor. This is why the method occasionally creates ambiguity in classification and accordingly leads to unexpected mode of deposition. This paper describes a generating an formula to decide the standard classification of four types Van's Hul distributions, taking into consideration quantitatively sediment-loss percent and capacity-inflow ratio as well as the shape of the reservoirs by multiple regression analysis using the least square method to get a better fit to the design curves. The result is expressed as $Y=-1.95+55.8X_1+0.14X_2+0.12X_3$ in which the the values of Y locate the standard type I through type IV in the range from ten to forty with the interval of ten. The regression analysis was correlated well with the standard errors of estimate of around two except for the case of the type IV. This formula does not give big difference from the Borland's work in general sityation, but it demonstrates acceptable results, giving somewhat precise replys for the specific reservoirs. Its application to the Soyang Lake, one of the largest reservoirs in the country, defined clearly the type II, while the original method located it in the boundary of the type II and type III.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.646-659
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• 2018

This study proposed "Reservoir Park", which added disaster prevention function to urban green spaces such as city parks through domestic and overseas related laws review, case studies, field trips. This is a combination of urban parks and reservoirs as urban planning facilities, which can provide both space for daily use by urban residents and disaster mitigation functions in case of emergency. In order to prevent flooding in urban areas due to climate change, facilities should be installed in the form of parks, etc., as the reservoir facility should be systematically reviewed together with urban planning facilities. However it was found that the reservoir park was not clear as a theme park. In this study, the types of storage facilities in urban areas were reclassified into five types of storage parks reflecting the characteristics of urban green spaces through domestic case studies and field trips. The classification of the reservoir parks is classified into 5 kinds such as ecological type, vegetation cover type, exercise facility type, underground burial type and hybrid type based on groundwater level, human use, and reservoir size. This classification system can be used to determine the types of facilities to be built after designating the location of future storage facilities.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1227-1229
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• 2003

Kojima Lake in Okayama prefecture is a freshing reservoir constructed adjacent to the oldest reclaimed land in Japan. This lake has a serious water quality problem because two urban rivers are flowing into it. In the present study, unsupervised classification was performed at intervals of several years using Landsat MSS data in the past 15 years. After geometric correction of these data, MSS data corresponding geographically to the field observation data were extracted and subjected to the multivariate analysis. Water quality distribution in the lake was estimated using the regression equation obtained as a result. In addition, two - dimensional and three-dimensional numerical simulations were performed and compared with the distribution obtained from the satellite images. Behavior of the reservoir flows is complicated and water quality distribution varies greatly with the flows. Here, I report the results of analysis on three factors, field observation, numerical simulation and satellite images.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.3
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• pp.17-30
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• 2004

This study aimed to develop an agricultural drought assessment methodology for irrigated paddy field districts from a single reservoir. Agricultural drought was defined as the reservoir storage shortage state that cannot satisfy water requirement from the paddy fields. The suggested model, SRADEMP (a Single Reservoir Agricultural Drought Evaluation Model for Paddy), was composed of 4 submodels: PWBM (Paddy Water Balance Model), RWBM (Reservoir Water Balance Model), FA (Frequency and probability Analysis model), and DCI (Drought Classification and Indexing model). Two indices, PDF (Paddy Drought Frequency) and PDI (Paddy Drought Index) were also introduced to classify agricultural drought severity Both values were divided into 4 steps, i.e. normal, moderate drought, severe drought, and extreme drought. Each step of PDI was ranged from +4.2 to -1.39, from -1.39 to -3.33, from -3.33 to -4.0 and less than -4.0, respectively. SRADEMP was applied to Jangheung reservoir irrigation district, and the results showed good relationships between simulated results and the observed data including historical drought records showing that SRADEMP explains better the drought conditions in irrigated paddy districts than PDSI.