• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.101-111
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• 2015

Stream restoration projects have become threats to riparian ecosystem in Rep. of korea. Riparian wildlife becomes isolated and the animals are often experience difficulties in crossing riparian corridors. The purposes of this study is to select suitable wildlife passages for wild animals crossing riparian corridors. Maximum entropy model and snow tracking data on embankment in winter seasons were used to develop species distribution models to select suitable wildlife passages for water deer. The analysis suggests the following. Firstly, most significant factors for water deer's habitat in area nearby riparian area are shown to distance to water, age-class, land cover, slope, aspect, digital elevation model, tree density, and distance to road. For the riparian area, significant factors are shown to be land cover, size of riparian area, distance to tributary, and distance to built-up. Secondly, the suitable wildlife passages are recommended to reflect areas of high suitability with Maximum Entropy model in riparian areas and the surrounding areas and moving passages. The selected suitable areas are shown to be areas with low connectivity due to roads and vertical levee although typical habitats for water deer are forest, grassland, and farmland. In addition, the analysis of traces on snow suggests that the water deer make a detour around the artificial structures. In addition, the water deer are shown to make a detour around the fences of roads and embankment around farmland. Lastly, the water deer prefer habitats around riparian areas following tributaries. The method used in this study is expected to provide cost-efficient and functional analysis in selecting suitable areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.53-69
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• 2020

The increase of the impermeable area due to industrialization and urban development distorts the hydrological circulation system and cause serious stream drying phenomena. In order to manage this, it is necessary to develop a technology for impact assessment of stream drying phenomena, which enables quantitative evaluation and prediction. In this study, the cause of streamflow reduction was assessed for dam and weir watersheds in the five major river basins of South Korea by using distributed hydrological model DrySAT-WFT (Drying Stream Assessment Tool and Water Flow Tracking) and GIS time series data. For the modeling, the 5 influencing factors of stream drying phenomena (soil erosion, forest growth, road-river disconnection, groundwater use, urban development) were selected and prepared as GIS-based time series spatial data from 1976 to 2015. The DrySAT-WFT was calibrated and validated from 2005 to 2015 at 8 multipurpose dam watershed (Chungju, Soyang, Andong, Imha, Hapcheon, Seomjin river, Juam, and Yongdam) and 4 gauging stations (Osucheon, Mihocheon, Maruek, and Chogang) respectively. The calibration results showed that the coefficient of determination (R²) was 0.76 in average (0.66 to 0.84) and the Nash-Sutcliffe model efficiency was 0.62 in average (0.52 to 0.72). Based on the 2010s (2006~2015) weather condition for the whole period, the streamflow impact was estimated by applying GIS data for each decade (1980s: 1976~1985, 1990s: 1986~1995, 2000s: 1996~2005, 2010s: 2006~2015). The results showed that the 2010s averaged-wet streamflow (Q95) showed decrease of 4.1~6.3%, the 2010s averaged-normal streamflow (Q185) showed decreased of 6.7~9.1% and the 2010s averaged-drought streamflow (Q355) showed decrease of 8.4~10.4% compared to 1980s streamflows respectively on the whole. During 1975~2015, the increase of groundwater use covered 40.5% contribution and the next was forest growth with 29.0% contribution among the 5 influencing factors.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.3
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• pp.21-29
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• 2017

In this study of visitor place attachment to parks, Scannel and Gilford considered the dimensions of place attachment, and proposed the theoretical framework of PPP(Place, Person, Process) that should be systematically studied from the perspective of person, place and psychological processes. According to Scannel's theoretical basis, this paper puts forward the hypothesis of a structure model of place attachment. In the model, the five independent variables of people, places, cognition, emotion, and behavior have influenced the dependent variable of place attachment. The questionnaire was conducted on 18 neighborhood parks in Kunming, China, and the residents of the 5 neighborhood parks in Daejeon, South Korea. A total of 1,645 valid samples of the questionnaire survey were collected. Through confirmatory factor analysis(CFA) results of the inspection of the various elements, it was shown that the reliabilities of 6 latent variables, such as people, places, cognition, affection, behavior and place attachment, which were composed with the observed variables(30 observed variables in Daejeon, 19 observed variables in Kunming), were all above 0.7 and the data were fit for this study. The hypothesis test results found that the physical environment of the neighborhood parks such as a comfortable environment, pleasant road and convenient facilities would increase the rate of visitors coming back. From the park management perspective, to increase the amount of visitors to the park, the park should increase visibility, provide more organized, varied activities and meetings, or special exhibitions according to the particular characteristics of the individual park, to increase awareness of the park. From the park visitors' psychological perspective, visitors are seeking to enjoy the park facilities and environment not only to bring physical relaxation, but also to bring about a psychological cure. With the commonality of attachment structure between the two countries, to improve the place attachment of neighborhood park visitors, collecting regularly visitor feedback will facilitate the sustainable development of neighborhood park attachment.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.97-110
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• 2012

In case of the axial line bikeway, the residents in the riverside use it frequently but those in the city center hardly do as the axis is formed along the riverside of the Han river. This situation happens due to the inefficient connectivity between the bikeway that leads to the riverside and that in the city center, which means the administrative policies have been focused on the facilities rather than on raising connectivity of the bikeway networks in the south of the Han River. Also, this is the point of time when a reasonable improvement plan is needed to raise the accessibilities of the bikeways in Seoul, not just a utilization of those in the south of the Han River. This study intends to analyse the connectivity between Gangseo (west of the Han River) and Gangdong (East of the Han River) districts by comparing the current spatial structure in the southern areas of the Han River and that after the installation of the axial line, and that after installation of the ordinary bikeway respectively with use of the Space Syntax Model, and to predict resultant changes there of. The Space Syntax Model was used as the major method of analysis in this study to find out characteristics of the bikeways in the south of the Han River. Consequently, as the analysis shows that when the bikeways in the Gangseo and Gangdong-gu districts are connected, the accessibility of the bikeways in the south of the Han River will improve while the spatial structure intelligibility will be lowered, it can be said the prediction rate in the all areas is low compared with the current status. It is analyzed that the intelligibility of the current bikeways in the south of the Han River appeared highest when compared with those of plans for installation of the axial line and ordinary bikeway because they don't interconnect with other areas.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.1 s.6
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• pp.43-56
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• 2005

This research has examined a time series analysis(TSA) of an every hour traffic information such as occupancy, a traffic flow, and a speed, a statistical model of a surveyed data on the traffic fundamental diagram and an expand aspect of a traffic jam by many Parts of the traffic flow. Based on the detected data from traffic accidents on the Cheonan-Nonsan high way and events when the road volume decreases dramatically like traffic accidents it can be estimated from the change of occupancy right after accidents. When it comes to a traffic jam like events the changing gap of the occupancy and the mean speed is gentle, in addition to a quickness and an accuracy of a detection by the time series analyse of simple traffic index is weak. When it is a stable flow a relationship between the occupancy and a flow is a linear, which explain a very high reliability. In contrast, a platoon form presented by a wide deviation about an ideal speed of drivers is difficult to express by a statical model in a relationship between the speed and occupancy, In this case the speed drops shifty at 6$\~$8$\%$ occupancy. In case of an unstable flow, it is difficult to adopt a statistical model because the formation-clearance Process of a traffic jam is analyzed in each parts. Taken the formation-clearance process of a traffic jam by 2 parts division into consideration the flow having an accident is transferred to a stopped flow and the occupancy increases dramatically. When the flow recovers from a sloped flow to a free flow the occupancy which has increased dramatically decrease gradually and then traffic flow increases according as the result analyzed traffic flow by the multi regime as time series. When it is on the traffic jam the traffic flow transfers from an impeded free flow to a congested flow and then a jammed flow which is complicated more than on the accidents and the gap of traffic volume in each traffic conditions about a same occupancy is generated huge. This research presents a need of a multi-regime division when analyzing a traffic flow and for the future it needs a fixed quantity division and model about each traffic regimes.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.233-254
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• 2017

The purpose of this study is to analyze the main factors influencing the severity of traffic accidents and to visualize spatiotemporal characteristics of traffic accidents in Seoul. To do this, we collected the traffic accident data that occurred in Seoul for four years from 2012 to 2015, and classified as slight, serious, and death traffic accidents according to the severity of traffic accidents. The analysis of spatiotemporal characteristics of traffic accidents was performed by kernel density analysis, hotspot analysis, space time cube analysis, and Emerging HotSpot Analysis. The factors affecting the severity of traffic accidents were analyzed using decision tree model. The results show that traffic accidents in Seoul are more frequent in suburbs than in central areas. Especially, traffic accidents concentrated in some commercial and entertainment areas in Seocho and Gangnam, and the traffic accidents were more and more intense over time. In the case of death traffic accidents, there were statistically significant hotspot areas in Yeongdeungpo-gu, Guro-gu, Jongno-gu, Jung-gu and Seongbuk. However, hotspots of death traffic accidents by time zone resulted in different patterns. In terms of traffic accident severity, the type of accident is the most important factor. The type of the road, the type of the vehicle, the time of the traffic accident, and the type of the violation of the regulations were ranked in order of importance. Regarding decision rules that cause serious traffic accidents, in case of van or truck, there is a high probability that a serious traffic accident will occur at a place where the width of the road is wide and the vehicle speed is high. In case of bicycle, car, motorcycle or the others there is a high probability that a serious traffic accident will occur under the same circumstances in the dawn time.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.3
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• pp.302-313
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• 2008

The purposes of this study are to investigate the concentration levels of fine particles, so called PM$_{2.5}$, to identify the affecting sources, and to estimate quantitatively the source contributions of PM$_{2.5}$. Ambient air sampling was seasonally carried out at two sites in Pohang(a residential and an industrial area) during the period of March to December 2003. PM$_{2.5}$ samples were collected by high volume air samplers with a PM$_{10}$ Inlet and an impactor for particle size segregation, and then determined by gravimetric method. The chemical species associated with PM$_{2.5}$ were analyzed by inductively coupled plasma spectrophotometery(ICP) and ion chromatography(IC). The results showed that the most significant season for PM$_{2.5}$ mass concentrations appeared to be spring, followed by winter, fall, and summer. The annual mean concentrations of PM$_{2.5}$ were 36.6 $\mu$g/m$^3$ in the industrial and 30.6 $\mu$g/m$^3$ in the residential area, respectively. The major components associated with PM$_{2.5}$ were the secondary aerosols such as nitrates and sulfates, which were respectively 4.2 and 8.6 $\mu$g/m$^3$ in the industrial area and 3.7 and 6.9 $\mu$g/m$^3$ in the residential area. The concentrations of chemical component in relation to natural emission sources such as Al, Ca, Mg, K were generally higher at both sampling sites than other sources. However, the concentrations of Fe, Mn, Cr in the industrial area were higher than those in the residential area. Based on the principal component analysis and stepwise multiple linear regression analysis for both areas, it was found that soil/road dust and secondary aerosols are the most significant factors affecting the variations of PM$_{2.5}$ in the ambient air of Pohang. The source apportionments of PM$_{2.5}$ were conducted by chemical mass balance(CMB) modeling. The contributions of PM$_{2.5}$ emission sources were estimated using the CMB8.0 receptor model, resulting that soil/road dust was the major contributor to PM$_{2.5}$, followed by secondary aerosols, vehicle emissions, marine aerosols, metallurgy industry. Finally, the application and its limitations of chemical mass balance modeling for PM$_{2.5}$ was discussed.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.8
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• pp.480-491
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• 2015

In this study, methods using LDC (Load Duration Curve) and watershed model were suggested to develope management targets and evaluate target achievement for non-point source pollution management considering watershed and runoff characteristics and possibility for achievement of target. These methods were applied for Saemangeum watershed which was designated as nonpoint source pollution management area recently. Flow duration interval of 5 to 40% was selected as flow range for management considering runoff characteristics and TP was selected as indicator for management. Management targets were developed based on scenarios for non-point source pollutant reduction of management priority areas using LDC method and HSPF model which was calibrated using 4 years data (2009~2012). In the scenario of LID, road sweeping and 50% reduction in CSOs and untreated sewage at Jeonju A20 and 30% reduction in fertilizer and 50% in livestock NPS at Mankyung C03, Dongjin A14 and KobuA14, management targets for Mangyung bridge, Dongjin bridge, Jeonju stream and Gunpo bridge were developed as TP 0.38, 0.18, 0.64 and 0.16 mg/L respectively. When TP loads at the target stations were assumed to have been reduced by a certain percentage (10%), management targets for those target stations were developed as TP 0.35, 0.17, 0.60 and 0.15 mg/L respectively. The result of this study is expected to be used as reference material for management master plan, implementation plan and implementation assessment for non-point source management area.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• Korean Journal of Environment and Ecology
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• v.35 no.4
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• pp.377-386
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• 2021

This study was conducted to provide basic data for habitat management and preservation of Jeongmaek. A total of 18 priority research areas were selected with consideration to terrain and habitat environment, and 54 fixed plots were selected for three types of habits: development, valley, and forest road and ridge. The survey was conducted in each season (May, August, and October), excluding the winter season, from 2016 to 2018. The distribution analysis of birds observed in each habitat type using a self-organizing map (SOM) classified them into a total of four groups (MRPP, A=0.12, and p <0.005). The comparative analysis of the number of species, the number of individuals, and the species diversity index for each SOM group showed that they were all the highest in group III (Kruskal-Wallis, the number species: x² = 13.436, P <0.005; the number of individuals: x² = 8.229, P <0.05; the species diversity index: x² = 17.115, P <0.005). Moreover, the analysis by applying the land cover map to the random forest model to examine the index species of each group and identify the characteristics of the habitat environment showed a difference in the ratio of the habitat environment and the indicator species among the four groups. The index species analysis identified a total of 18 bird species as the indicator species in three groups except for group II. When applying the random forest model and indicator species analysis to the results of classification into four groups using the SOM, the composition of the indicator species by the group showed a correlation with the habitat characteristics of each group. Moreover, the distribution patterns and densities of observed species were clearly distinguished according to the dominant habitat for each group. The results of the analysis that applied the SOM, indicator species, and random forest model together can derive useful results for the characterization of bird habitats according to the habitat environment.