• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.363-373
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• 2021

An ecological niche is defined as the specific role of a species influenced by time, space, and other resources. By investigating overlaps between ecological niches of different species, we could estimate the degrees of interspecific competition. Such studies often use geographic information systems (GIS) to discover niche overlaps between species. In this study, we used GIS to estimate the spatial niches of two Korean frog species(Pelophylax nigromaculatus and P. chosenicus). This enabled us to predict their geographic distributions in order to identify their coexistence regions and distribution patterns. The results confirmed that altitude was an important variable for predicting their distribution, with a correlation with their climatic range. Spatial distributions of the two frog species were highly overlapped, as the distribution range for P. nigromaculatus included most of the range of P. chosenicus, showing a sympatric distribution pattern. Within the coexisting regions, however, the presence sites for the two species did not overlap, implying weak competition. To confirm the principal factors influencing their competitive relationship and reasons for their sympatric distribution pattern, we need more detailed in-depth studies on the diverse environmental variables within the regions where the two species coexist. By doing so, we would be able to identify various mechanisms for avoiding competition in sympatric frog species.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.87-96
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• 2020

To use a hydraulic structure effectively, the velocity of a river should be known in detail. In reality, velocity measurements are not conducted sufficiently because of their high cost. The formulae to yield the flux and velocity of the river are commonly called the Manning and Chezy formulae, which are empirical equations applied to uniform flow. This study is based on Chiu (1987)'s paper using entropy theory to solve the limits of the existing velocity formula and distribution and suggests the velocity and distance formula derived from information entropy. The data of a channel having records of a spot's velocity was used to verify the derived formula's utility and showed R2 values of distance and velocity of 0.9993 and 0.8051~0.9483, respectively. The travel distance and velocity of a moving spot following the streamflow were calculated using some flow information, which solves the difficulty in frequent flood measurements when it is needed. This can be used to make a longitudinal section of a river composed of a horizontal distance and elevation. Moreover, GIS makes it possible to obtain accurate information, such as the characteristics of a river. The connection with flow information and GIS model can be used as alarming and expecting flood systems.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.407-415
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• 2008

This study presents the methodology to link with aerial photos for advancing the accuracy of topographic survey data that is used to calculate water volume in urban stream. First, GIS spatial interpolation technique as Inverse Distance Weight (IDW) and Kriging was applied to construct the terrain morphology to the sand-bar and grass area using cross-sectional survey data, and also validation point data was used to estimate the accuracy of created topographic data. As the result of comparison, IDW ($d^{-2}_{ij}$, 2nd square number) in Sand-bar area and Kriging Spherical model in grass area showed more efficient results in the construction of topographic data of river boundary. But the differences among interpolation methods are very slight. Image classification method, Minimum Distance Method (MDM) was applied to extract sand-bar and grass area that are located to river boundary efficiently and the elevation value of extracted layers was allocated to the water level point value. Water volume with topographic data from aerial photos shows the advanced accuracy of 13% (in sand-bar) and 12% (in grass) compared to the water volume of original terrain data. Therefore, terrain analysis method in river linking with aerial photos is efficient to the monitoring about sand-bar and grass area that are located in the downstream of Dam in flooding season, and also it can be applied to calculate water volume efficiently.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.571-580
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• 2012

Epidemiologic studies of air pollution need accurate exposure assessments at unmonitored locations. A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe, North America, and a few Asian region. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Metropolitan area in Korea. We used the annual-mean nitrogen dioxide ($NO_2$) in 2010 in the study area. Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and elevation. Using geographical variables, we then constructed a model to predict the monitored levels of $NO_2$. The mean concentration of $NO_2$ was 30.71 ppb (standard deviation of 5.95) respectively. The final regression model for the $NO_2$ concentration included five independent variables. The LUR models resulted in $R^2$ of 0.59. The mean concentration of $NO_2$ of elementary schools was 34.04 ppb (standard deviation of 5.22) respectively. The present study showed that even if we used regulatory monitoring air quality data, we could estimate $NO_2$ moderately well. These analyses confirm the validity of land use regression modeling to assign exposures in epidemiological studies, and these models may be useful tools for assessing health effects of long-term exposure to traffic related pollution.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.545-555
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• 2020

A feasibility modeling for potential hydroelectric dam site selection was suggested using 1 sec ASTGTM (ASTER Global Digital Elevation Model) and Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) derived land use (MCD12Q1) data. The modeling includes DEM pre-processing of peak, sink, and flat, river network generation, watershed delineation and segmentation, terrain analysis of stream cross section and reservoir storage, and estimation of submerged area for compensation. The modeling algorithms were developed using Python and as an open source GIS. When a user-defined stream point is selected, the model evaluates potential hydroelectric head, reservoir surface area and storage capacity curve, watershed time of concentration from DEM, and compensation area from land use data. The model was tested for 4 locations of already constructed Buhang, BohyunMountain, Sungdeok, and Yeongju dams. The modeling results obtained maximum possible heads of 37.0, 67.0, 73.0, 42.0 m, surface areas of 1.81, 2.4, 2.8, 8.8 ㎢, storages of 35.9, 68.0, 91.3, 168.3×10⁶ ㎥ respectively. BohyunMountain and Sungdeok show validity but in case of Buhang and Yeongju dams have maximum head errors. These errors came from the stream generation error due to ASTGTM. So, wrong dam watershed boundary limit the head. This study showed a possibility to estimate potential hydroelectric dam sites before field investigation especially for overseas project.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.107-118
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• 2003

The purpose of this study is to assess the hydrological impact on a watershed from long-term land cover changes. Gyeongan-cheon watershed($558.2km^2$) was selected and WMS(watershed modeling system) HEC-1 model was adopted as an evaluation tool. To identify land cover changes, five Landsat images(1980/2/15, 1986/4/15, 1990/4/26, 1996/4/26, 2000/5/17) were selected and analyzed using maximum likelihood method. As a result, urban areas have increased by 5.6% and forest areas have decreased by 6.1% between 1980 and 2000. SCS curve number increased by 9.8. To determine model parameters and evaluate HEC-1 model, five storm events(1998/5/2, 1998/8/23, 1998/9/30, 1999/5/3, 2000/7/29) were used. The simulated stream flow agreed well with the observed one with relative errors ranging from 9% to 36%. For 254 mm daily rainfall of 30 years frequency, due to the increase of urban areas peak flow increased by $455m^3/sec$ and the time of peak flow reduced about four hours for 20 years land cover changes.

• Journal of Preventive Medicine and Public Health
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• v.51 no.1
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• pp.33-40
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• 2018

Objectives: The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC) in the neighborhoods of Tehran, Iran using Bayesian spatial models. Methods: This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The $Besag-York-Molli{\acute{e}}$ (BYM) model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. Results: The Moran index was statistically significant for all the variables studied (p<0.05). The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53), living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96), not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94) and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68) were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range) and mean (standard deviation) values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01) and 1.05 (1.31), respectively. Conclusions: Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in at-risk areas.

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.823-832
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• 2006

Precipitation is the most important component to the study of water and energy cycle in hydrology. In this study we investigate rainfall retrieval uncertainty from different sources of remotely sensed precipitation field and then probable error propagation in the simulation of hydrologic variables especially, runoff on different vegetation cover. Two remotely sensed rainfall retrievals (space-borne IR-only and ground radar rainfall) are explored and compared visually and statistically. Then, an offline Community Land Model (CLM) is forced with in situ meteorological data to simulate the amount of runoff and determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes. Results show there are big differences between different sources of precipitation fields in terms of the magnitude and temporal variability. The study provides some intuitions on the uncertainty of hydrologic prediction via the interaction between the land surface and near atmosphere fluxes in the modelling approach. Eventually it will contribute to the understanding of water resources redistribution to the climate change in Korean Peninsula.

• Journal of Environmental Impact Assessment
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• v.15 no.1
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• pp.53-65
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• 2006

Topographic condition is one of the most important things in farming activities. The topographic condition didn't matter for farming in the past because agricultural products had competitive power in the market. So farmers tried to extend their farms without any concern of topographic condition. We need less labor-consuming farming as industrial structure has been changed and the competitive power of the farming has been getting weak. This study analyzed the fallow potential in agricultural area by topographic condition so that we have got results as follows. Maps of elevation, slope, distance from roads and water resources were made for getting a fallow probability model in farms, and these 4 factors were used as independent variables while a variable on whether it is fallow or not is a dependent variable in logistic regression model. In an analysis of the fallow potential depending on farm land types, the fallow probability in fallow orchard showed the highest value of farm lands, 0.973. Cultivated orchard had 0.730 and upland had 0.616 of the fallow probability. The fields having high fallow potential had high elevation, steep slope, and long distance from water resources and roads. Especially, fields having a probability over 0.99 appeared in orchards, fallow uplands and single cropping uplands, which were recognized to have several disadvantages related to the fallow like as high elevation, steep slope, and long distance from water resources and roads. With the logistic analysis, the suitable farm lands appeared at 16.45m of the mean elevation, 1.89 degree of the mean slope, 39.91m of the average distance from water resources, and 32.39m of the average distance from roads. On the contrary, non-suitable land appeared at 114.7m of the mean elevation, 24.9 degree of the mean slope. The distance from roads was more important variable than the distance from water resources for analyzing suitable farm land.