• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.9 no.1
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• pp.20-29
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• 2004

Macrobenthos species-area relationship was investigated and empirical models were estimated to enable comparisons among species numbers of different sample size. The study aims to choose a way to predict cumulative relative species number (CRSN) in a given sample size Saemangeum, located in the west coast of South Korea, were visited in Apr., May and Aug.,2002 and a total of 261 biological samples from 87 stations were obtained by employing a quantitative sediment sampler, Smith-McIntyre grab and design of 3 replicates at each station. Relative species numbers (%) were baselined at sample size of 1000 $\textrm{cm}^2$ and the patterns of CRSN along the axis of sample size were measured and observed. In correlation analysis performed on a set of abiotic and biotic variables, log-transformed CRSN showed the only significant relationship with log-transformed density. Based on the result, three models, Log CRSN 2000, Log CRSN 3000 and Log CRSN were produced. The former two were devised to predict CRSN at 2000 and 3000 $\textrm{cm}^2$ respectively, and the latter at various sample sizes and samplers (all p-values were <0.001). Database from other studies (intertidal or subtidal macrofaunal samples from Kyonggi Bay and Saemangeum) were used to evaluate validity of the models. Observed CRSN below sample size of 3000 $\textrm{cm}^2$ fell under the range of 95% prediction interval and this was appeared to provide reliability of the models below that sample size.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.169-186
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• 2018

The social influence of the elderly population will accelerate in a rapidly aging society. The purpose of this study is to establish a methodology for extracting vulnerable districts of the welfare of the aged through machine learning(ML), artificial neural network(ANN) and geospatial analysis. In order to establish the direction of analysis, this progressed after an interview with volunteers who over 65-year old people, public officer and the manager of the aged welfare facility. The indicators are the geographic distance capacity, elderly welfare enjoyment, officially assessed land price and mobile communication based on old people activities where 500 m vector areal unit within 15 minutes in Yongin-city, Gyeonggi-do. As a result, the prediction accuracy of 83.2% in the support vector machine(SVM) of ML using the RBF kernel algorithm was obtained in simulation. Furthermore, the correlation result(0.63) was derived from ANN using backpropagation algorithm. A geographically weighted regression(GWR) was also performed to analyze spatial autocorrelation within variables. As a result of this analysis, the coefficient of determination was 70.1%, which showed good explanatory power. Moran's I and Getis-Ord Gi coefficients are analyzed to investigate spatially outlier as well as distribution patterns. This study can be used to solve the welfare imbalance of the aged considering the local conditions of the government recently.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.307-313
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• 2014

In order to predict the post-cracking tensile behavior of fiber reinforced concrete, it is necessary to evaluate the fiber orientation factor which indicates the number of fibers bridging a crack. For investigation of fiber orientation factor on a circular cross-section, in this paper, cylindrical steel fiber reinforced concrete specimens were casted with the variables of concrete compressive strength, circular cross-section size, fiber type, and fiber volumetric ratio. The specimens were cut perpendicularly to the casting direction so that the fiber orientation factor could be evaluated through counting the number of fibers on the circular cross-section. From the test results, it was investigated that the fiber orientation factor on a circular cross-section was lower than 0.5 generally adopted, as fibers tended to be perpendicular to the casting direction. In addition, it was observed that the fiber orientation factor decreased with an increase of the number of fibers per unit cross-section area. For rational prediction of the fiber orientation factor on a circular section, a rigorous model and a simplified equation were derived through taking account of a possible fiber inclination angle considering the circular boundary surface. From the comparison of the measured data and the predicted values, it was found that the fiber orientation factor was well predicted by the proposed model. The test results and the proposed model can be useful for researches on structural behavior of steel fiber reinforced columns with a circular cross-section.

• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.272-287
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• 2013

This paper describes the importance of selecting explanatory variables(e.g. land price determinants) in hedonic pricing models employed in predicting real estate price, and explores dynamics of the land price determinants over time. The City of Junju was chosen as the study area, and repeated measured price data of standard lots over 17 years were analyzed. We applied a three-level modeling approach to this data in consideration of its nested data structure and longitudinal characteristics. Main land price determinants we focused on are primarily based on items included in the standard comparison table of land price, which is an official hedonic pricing model used by Government to estimate land price for tax levy. Our result shows that the land price fluctuation over 17 years was not uniform over the whole study area with each neighborhood revealing different price trend, and as such warrants longitudinal model components. In addition, some of determinants previously recognized as important were proved insignificant. It was also found that significant determinants at a particular time point lost its power gradually over time and vice versa. It is expected that more accurate prediction of price would be possible when taken account for this dynamics of price determinants over time in applying hedonic pricing model method.

• Journal of the Korea Concrete Institute
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• v.26 no.6
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• pp.731-739
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• 2014

This paper describes the evaluation of the impact performance of steel fiber-reinforced concrete based on high-velocity impact experiments using hard spherical balls. In this experimental study, panel specimens with panel thickness to ball diameter (h/d) ratios of 3.5 or less were tested with variables of steel fiber volume fraction, panel thickness, impact velocity, and aggregate size. Test results were compared with each other to evaluate the impact resistance. The results showed that the percentage of weight and surface loss decreased as the steel volume fraction increased. However, the penetration depth increased with up to steel fiber volume fraction of 1.5%. Particularly the results of specimens with 20 mm aggregates showed poorer performance than those with 8 mm aggregates. The results also confirmed that the impact performance prediction formulas are conservative with (h/d) ratios of 3.5 or less. Despite the conservative predictions, the modified NDRC formula and ACE formula predict the impact performance more consistently than the Hughes formula.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.687-701
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• 2014

Sea ice is generally accepted as an important factor to understand the process of earth climate changes and is the basis of earth system models for analysis and prediction of the climate changes. To continuously monitor sea ice changes at kilometer scale, it is demanded to create more accurate grid data from the current, limited sea ice data. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Sea Ice Concentration (SIC) from 10 km to 1 km resolution using a weighting scheme of sea ice days ratio derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice cover product that has a high correlation with the SIC. In a case study for Okhotsk Sea, the sea ice areas of both data (before and after downscaling) were identical, and the monthly means and standard deviations of SIC exhibited almost the same values. Also, Empirical Orthogonal Function (EOF) analyses showed that three kinds of SIC data (ERA-Interim, original AMSR2, and downscaled AMSR2) had very similar principal components for spatial and temporal variations. Our method can apply to downscaling of other continuous variables in the form of ratio such as percentage and can contribute to monitoring small-scale changes of sea ice by providing finer SIC data.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.5
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• pp.527-534
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• 2019

Several types of mathematical analysis methods are used for port waterway risk assessment based on marine traffic volume. In Korea, a marine traffic congestion model that standardizes the size of the vessels passing through the port waterway is applied to evaluate the risk of the waterway. For example, when marine traffic congestion is high, risk situations such as collisions are likely to occur. However, a scientific review is required to determine if there is a correlation between high density of maritime traffic and a high risk of waterway incidents. In this study, IWRAP Mk2(IALA official recommendation evaluation model) and a marine traffic congestion model were used to analyze the correlation between port waterway risk and marine traffic congestion in the same area. As a result, the linear function of R2 was calculated as 0.943 and it was determined to be significant. The Pearson correlation coefficient was calculated as 0.971, indicating a strong positive correlation. It was confirmed that the port waterway risk and the marine traffic congestion have a strong correlation due to the influence of the common input variables of each model. It is expected that these results will be used in the development of advanced models for the prediction of port waterway risk assessment.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.432-441
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• 2016

In this study, we predicted species distributions in Mt. Inwang and Mt. An as preceding research to build ecological corridor by considering connectivity of habitats which have been fragmented in the city. We analyzed species distributions by using Maxent (Maximum Entropy Approach) model with species presence. We used 23 points of mammals and 15 points of Titmouse (Parus major, P. palustris, P. varius) as target species from appearance points of species examined. We build 4 geography factors, 4 vegetation factors, and 2 distance factors as model variables In case of mammals, factors that affected species distribution model was Digital Elevation Model(DEM, 34%) followed by Distance from edge forest to interior (24.8%) and Species of tree (10%). On the other hand, in case of Parus species, factors that affected species distribution model were DEM (39.6%) followed by distance from road (35.4%) and Density-class (8.2%). Therefore, birds and mammals prefer interior of mountain, and this area needs to be protected.