• Journal of Wetlands Research
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• v.18 no.4
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• pp.394-403
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• 2016

This study was carried out to verify change and relationship between the concentration of chlorophyll-a and environmental factors including weather, water quality and discharge at before & after Gangcheon and Ipoh weir construction at Namhan river, based on the weather and water quality data provided by the measuring network. We classified the period of before & after weir construction by the cluster analysis with Ward's method, and also through the correlation analysis between the concentration of chlorophyll-a and environmental factors, the influence factors related with algae occurrence(Chlorophyll-a) were analyzed. The result by cluster analysis based on data of the total 12 factors (water temperature, rainfall, daylight, pH, DO, BOD, COD, T-N, $NH_3-N$, $NO_3-N$, T-P, $PO_4-P$) from 2005 to 2015 indicated a clear classification into two periods, before(2006-2007) & after (2012-2013) weir construction. After weir construction, class of BOD at Gangcheon weir was better than before, changed from II class to Ia class, and likewise class of BOD at Ipoh weir was improved from II-III class to Ia-IIclass. Also T-P and T-N concentration also were to be improved in general after weir construction. Concentraion of Chlorophyll-a afterGangcheon and Ipoh weir construction was to be decreased. However, frequency of algae warning was increased from 9 to 15 after Ipoh weir construction due to increasing of HRT and water temperature. After weirs construction, the result of correlation analysis between weather, water quality and discharge and concentration of chlorophyll-a indicated a positive correlation, order of BOD(0.579) > COD(0.413) > temperature(0.237), and a negative correlation, order of $NO_3-N$(-0.344) > T-N(-0.293) at Gangcheon weir. And there were likewise positive correlation, order of BOD(0.795) > pH(0.581) > Water temperature(0.422), and negative correlation, order of $NO_3-N$(-0.457) > T-N(-0.371) > $NH_3-N$(-0.326) > $PO_4-P$(-0.288) > Discharge(-0.213) after Ipoh weir construction. Although water quality after Ipoh weir construction was generally improved, increase of frequency of algae warning occurrence was influenced by change of water conditions such as reduction of the velocity, increase of HRT and water temperature, etc impacted strongly by change of the stream flow more than change of water environments after weir construction.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.597-611
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• 2013

This paper presents the methodology for construction of time-area curve via the width function and thereby rational estimation of time of concentration and storage coefficient of Clark model within the framework of method of moments. To this end time-area curve is built by rescaling the grid-based width function under the assumption of pure translation and then the analytical expressions for two parameters of Clark model are proposed in terms of method of moments. The methodology in this study based on the analytical expressions mentioned before is compared with both (1) the traditional optimization method of Clark model provided by HEC-1 in which the symmetric time-area curve is used and the difference between observed and simulated hydrographs is minimized (2) and the same optimization method but replacing time-area curve with rescaled width function in respect of peak discharge and time to peak of simulated direct runoff hydrographs and their efficiency coefficient relative to the observed ones. The following points are worth of emphasizing: (1) The optimization method by HEC-1 with rescaled width function among others results in the parameters well reflecting the observed runoff hydrograph with respect to peak discharge coordinates and coefficient of efficiency; (2) For the better application of Clark model it is recommended to use the time-area curve capable of accounting for irregular drainage structure of a river basin such as rescaled width function instead of symmetric time-area curve by HEC-1; (3) Moment-based methodology with rescaled width function developed in this study also gives rise to satisfactory simulation results in terms of peak discharge coordinates and coefficient of efficiency. Especially the mean velocities estimated from this method, characterizing the translation effect of time-area curve, are well consistent with the field surveying results for the points of interest in this study; (4) It is confirmed that the moment-based methodology could be an effective tool for quantitative assessment of translation and storage effects of natural river basin; (5) The runoff hydrographs simulated by the moment-based methodology tend to be more right skewed relative to the observed ones and have lower peaks. It is inferred that this is due to consideration of only one mean velocity in the parameter estimation. Further research is required to combine the hydrodynamic heterogeneity between hillslope and channel network into the construction of time-area curve.

• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.289-301
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• 2023

Forests within watersheds are essential in maintaining ecosystems and are the central infrastructure for constructing an ecological network system. However, due to indiscriminate development projects carried out over past decades, forest fragmentation and land use changes have accelerated, and their original functions have been lost. Since a forest's structural pattern directly impacts ecological processes and functions in understanding forest ecosystems, identifying and analyzing change patterns is essential. Therefore, this study analyzed structural changes in the forest landscape according to the time-series land cover changes using the FRAGSTATS model for the dam watershed of the Geum River upstream. Land cover changes in the dam watershed of the Geum River upstream through land cover change detection showed an increase of 33.12 square kilometers (0.62%) of forests and 67.26 square kilometers (1.26%) of urbanized dry areas and a decrease of 148.25 square kilometers (2.79%) in agricultural areas from the 1980s to the 2010s. The results of no-sampling forest landscape analysis within the watershed indicated landscape percentage (PLAND), area-weighted proximity index (CONTIG_AM), average central area (CORE_MN), and adjacency index (PLADJ) increased, and the number of patches (NP), landscape shape index (LSI), and cohesion index (COHESION) decreased. Identification of structural change patterns through a moving window analysis showed the forest landscape in Sangju City, Gyeongsangbuk Province, Boeun County in Chungcheongbuk Province, and Jinan Province in Jeollabuk Province was relatively well preserved, but fragmentation was ongoing at the border between Okcheon County in Chungcheongbuk Province, Yeongdong and Geumsan Counties in Chungcheongnam Province, and the forest landscape in areas adjacent to Muju and Jangsu Counties in Jeollabuk Province. The results indicate that it is necessary to establish afforestation projects for fragmented areas when preparing a future regional forest management strategy. This study derived areas where fragmentation of forest landscapes is expected and the results may be used as basic data for assessing the health of watershed forests and establishing management plans.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.499-505
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• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.131-142
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• 2015

In order to support the development of a cost-effective river bathymetric system, this research has focused on modeling GPS observables, which are obtained by array of five single-frequency receivers (i.e., two references and three rovers) to estimate the high accurate kinematic position, and the surveying vessel altitude. Also, by applying all GPS measurements as multiple-baselines with constraining rover baselines, we derived the socalled ‘kinematic network model.’ From the model, the integer-constrained least-squares (LS) for position estimation and the implicit LS for attitude determination were implemented, while a series of simulation tests with respect to the baseline lengths around 2km performed to demonstrate its accuracy analysis. The on-the-fly (OTF) ambiguity resolution tests revealed that ninety-nine percents of time-to-fix-first ambiguity (TTFF) can be decided in less than two seconds, when the positioning accuracy of ambiguity-fixed solutions was assessed as the greater than or equal to one and two centimeters in horizontal and vertical, respectively. Comparing to the GPS-derived attitudes, the achievable accuracy gradually descended in sequence of yaw, pitch and roll due to the antenna geometric configuration. Furthermore, the RMSE values for the baseline lengths of three to six meters were within ±1′for yaw, and less than ±10′and ±20′for pitch and roll, respectively, but those of between six to fifteen meters were less than ±1′for yaw, ±5′for pitch, and ±10′for roll.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Journal of the Korean association of regional geographers
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• v.19 no.2
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• pp.352-361
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• 2013

This paper aims to analyze the root mean square errors of MODIS LST data and inland water temperature measurement data in order to use MODIS LST data as an input of numerical weather prediction model. MODIS LST data from July 2011 to June 2012 were compared to water temperature measurement data in the automated water quality measurement network. MODIS data have two composites: day-time and night-time. Monthly errors of day-time and night-time LST range $2{\sim}8^{\circ}C$ and $3{\sim}12^{\circ}C$, respectively. Temporally, monthly errors of day-time LST are less in fall and those of night-time LST are less in summer. Spatially, on the four major rivers including the Han, Nakdong, Geum, and Yeongsan rivers, the errors of Yeongsan river were the smallest, which location is the south-most among them. In this study, the errors of MODIS LST as an input of numerical weather prediction model were analyzed and the results can be used as an error level of MODIS LST data for inaccessible areas such as North Korea.

• Korean Journal of Ecology and Environment
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• v.38 no.4 s.114
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• pp.445-453
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• 2005

Relative importance between bottom-up and top-down controls on phytoplankton dynamics was investigated in the Juam Reservoir, Chonnam based on the results from statistical analyses including regression and artificial neural network (ANN) modeling. Effects of nutrients on size-structured phytoplankton dynamics were explored by simple linear regression analysis and relative importance between bottom-up and top-down controls was estimated based on results from the artificial neural network analyses. Although there is a limitation in determining direct grazing effects since chlorophyll a : pheopigments ratios, indirect index for grazing activity rather than grazing rates or herbivores biomass were used, the results from regression analysis showed that nutrients especially orthophosphates were positively correlated with the phytoplankton biomass and chlorophyll a : pheopigments ratios were also positively correlated with the phytoplankton biomass at lower coefficient of determination ($r^2$) compared to orthophosphates. The simulation results from ANN suggested that the bottom-up mechanisms including water temperature and availability of nutrients, especially orthophosphates were more important than top-down mechanisms such as grazing in the phytoplankton dynamics.

• Korean Journal of Ecology and Environment
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• v.51 no.1
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• pp.124-134
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• 2018

We analyzed research topics, temporal distribution of field sites, and researcher network of 1,508 limnology publications in the Korean Journal of Limnology (1968~2012) and the Korean Journal of Ecology and Environment (2013~2017). We found that water quality and sediment, phytoplankton, invertebrates, and fish were major subjects during the study periods. Survey of flora and fauna and physiological experiment of freshwater species were the largest subjects during 1970~80s, while other subjects including production, behavior, modeling, and ecological assessment have been rapidly increased since the 1990s. Most of the biological taxa equally studied lotic and lentic system, however, invertebrates and fish related studies more focused on the lotic system. Spatially, the field site of Korean limnology studies was found to be concentrated in main river channels runs through urban areas and artificial lakes than preserved natural areas. Freshwater system, located at the elevation range of 301~400 m (upstream of main channels), had the lowest number of field sites. Collaboration among researchers and different institution types have been steadily increased and expanded as the number of publications increased.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.341-351
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• 2008

Since the damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large river. Through the preprocess and main processes for estimating runoff, diverse errors occur and are accumulated, so that the outcome contains the errors in the existing flood forecasting and warning method. And estimating the parameters needed for runoff models requires a lot of data and the processes contain various uncertainty. In order to overcome the difficulties of the existing flood forecasting and warning system and the uncertainty problem, ANFIS(Adaptive Neuro-Fuzzy Inference System) technique has been presented in this study. ANFIS, a data driven model using the fuzzy inference theory with neural network, can forecast stream level only by using the precipitation and stream level data in catchment without using a lot of physical data that are necessary in existing physical model. Time series data for precipitation and stream level are used as input, and stream levels for t+1, t+2, and t+3 are forecasted with this model. The applicability and the appropriateness of the model is examined by actual rainfall and stream level data from 2003 to 2005 in the Tancheon catchment area. The results of applying ANFIS to the Tancheon catchment area for the actual data show that the stream level can be simulated without large error.