Proceedings of the Korea Water Resources Association Conference
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2002.05a
/
pp.43-50
/
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.
Increased base cation loss and Al mobilization, a consequence of soil acid neutralization responses, are common in air polluted areas showing forest decline. The prediction models of acid neutralization responses were developed by using indicators of soil acidification level(pH, and base saturation) in order to assess the forest soil sensitivity to acidification. The soil acidification level was greatest in Namsan followed by Kanghwa, Ulsan, and Hongcheon, being contrary to regional total ANCH pattern through soil columns leached with additional acid (16.7mmolcH+/kg), Both base exchange and Al dissolution were main acid neutralization processes in all study regions. There were low base exchange and high Al dissolution in the regions of the low total ANCH. The ANCM by sulfate adsorption was greatest in Hongcheon compared with other regions even though the AN rate was very low as 6.4%. Coefficients of adjusted determination of simple and multiple regression models between soil acidification level indicators and the acid neutralization responses were more than 0.52(p<0.04) and 0.89(p<0.01), respectively. The result suggests that soil pH and base saturation are available indicators for predicting the acid neutralization responses. These prediction models could be used as an useful method to measure forest soil sensitivity to acidification.
Nam-Hyuk Seo;Jun-Hyub Jeon;Soo-Yeong Song;Jong-Soo Kim;Min-Su Kim
Journal of Korea Foundry Society
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v.43
no.6
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pp.279-285
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2023
In order to predict the mechanical properties of ductile cast iron heat treated in an intercritical temperature range, samples machined from cast iron with a tensile strength of 450 MPa were heat-treated at various intercritical temperatures and air-cooled, after which a microstructural analysis and Brinell hardness test were conducted. As the heat treatment temperature was increased in the intercritical temperature range, the ferrite fraction in the ductile cast iron decreased and the pearlite fraction increased, whereas the nodularity and nodule count did not change considerably from the corresponding values in the as-cast condition. The Brinell hardness values of the heat-treated ductile cast iron increased gradually as the heat treatment temperature was increased. Based on the measured alloy composition, the fraction of each stable phase and the hardness model from the literature, the hardness of the ductile cast iron heat treated in the intercritical temperature range was calculated, showing values very similar to the measured hardness data. In order to check whether it is possible to predict the hardness of heat-treated ductile cast iron by using the phase fraction obtained from thermodynamic calculations, the volumes of graphite, ferrite, and austenite in the alloy were calculated for each temperature condition. Those volume fractions were then converted into areas of each phase for hardness prediction of the heat-treated ductile cast iron. The hardness values of the cast iron samples based on thermodynamic calculations and on the hardness prediction model were similar within an error range up to 27 compared to the measured hardness data.
Journal of the Korean Association of Geographic Information Studies
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v.20
no.4
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pp.89-101
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2017
Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.
One of the applications of geomagnetic paleo-secular variation (PSV) is the age dating of archeological remains (i.e., the archeomagnetic dating technique). This application requires the local model of PSV that reflects non-dipole fields with regional differences. Until now, the tentative Korean paleosecular variation (t-KPSV) calculated based on JPSV (SW Japanese PSV) has been applied as a reference curve for individual archeomagnetic directions in Korea. However, it is less reliable due to regional differences in the non-dipole magnetic field. Here, we present PSV curves for AD 1 to 600, corresponding to the Korean Three Kingdoms (including the Proto Three Kingdoms) Period, using the results of archeomagnetic studies in the Korean Peninsula and published research data. Then we compare our PSV with the global geomagnetic prediction model and t-KPSV. A total of 49 reliable archeomagnetic directional data from 16 regions were compiled for our PSV. In detail, each data showed statistical consistency (N > 6, 𝛼95 < 7.8°, and k > 57.8) and had radiocarbon or archeological ages in the range of AD 1 to 600 years with less than ±200 years error range. The compiled PSV for the initial six centuries (KPSV0.6k) showed declination and inclination in the range of 341.7° to 20.1° and 43.5° to 60.3°, respectively. Compared to the t-KPSV, our curve revealed different variation patterns both in declination and inclination. On the other hand, KPSV0.6k and global geomagnetic prediction models (ARCH3K.1, CALS3K.4, and SED3K.1) revealed consistent variation trends during the first six centennials. In particular, the ARCH3K.1 showed the best fitting with our KPSV0.6k. These results indicate that contribution of the non-dipole field to Korea and Japan is quite different, despite their geographical proximity. Moreover, the compilation of archeomagnetic data from the Korea territory is essential to build a reliable PSV curve for an age dating tool. Lastly, we double-check the reliability of our KPSV0.6k by showing a good fitting of newly acquired age-controlled archeomagnetic data on our curve.
KSCE Journal of Civil and Environmental Engineering Research
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v.35
no.6
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pp.1297-1308
/
2015
In Korea, the local traffic safety master plan has been established and implemented according to the Traffic Safety Act. Each local government is required to establish a customized traffic safety policy and share roles for improvement of traffic safety and this means that local governments lead and promote effective local traffic safety policies fit for local circumstances in substance. For implementing efficient traffic safety policies, which accord with many-sided characteristics of local governments, the prediction of community-based traffic accidents, which considers local characteristics and the analysis of accident influence factors must be preceded, but there is a shortage of research on this. Most of existing studies on the community-based traffic accident prediction used social and economic variables related to accident exposure environments in countries or cities due to the limit of collected data. For this reason, there was a limit in applying the developed models to the actual reduction of traffic accidents. Thus, this study developed a local traffic accident prediction model, based on smaller regional units, administrative districts, which were not omitted in existing studies and suggested a method to reflect traffic safety facility and policy variables that traffic safety policy makers can control, in addition to social and economic variables related to accident exposure environments, in the model and apply them to the development of local traffic safety policies. The model development result showed that in terms of accident exposure environments, road extension, gross floor area of buildings, the ratio of bus lane installation and the number of crossroads and crosswalks had a positive relation with accidents and the ratio of crosswalk sign installation, the number of speed bumps and the results of clampdown by police force had a negative relation with accidents.
Background: The diffusing capacity of the lung is influenced by multiple factors such as age, sex, height, weight, ethnicity and smoking status. Although a prediction equation for the diffusing capacity of Korea was proposed in the mid-1980s, this equation is not used currently. The aim of this study was to develop a new prediction equation for the diffusing capacity for Koreans. Methods: Using the data of the Korean National Health and Nutrition Examination Survey, a total of 140 nonsmokers with normal chest X-rays were enrolled in this study. Results: Using linear regression analysis, a new predicting equation for diffusing capacity was developed. For men, the following new equations were developed: carbon monoxide diffusing capacity (DLco)=-10.4433-0.1434×age (year)+0.2482×heights (cm); DLco/alveolar volume (VA)=6.01507-0.02374×age (year)-0.00233×heights (cm). For women the prediction equations were described as followed: DLco=-12.8895-0.0532×age (year)+0.2145×heights (cm) and DLco/VA=7.69516-0.02219×age (year)-0.01377×heights (cm). All equations were internally validated by k-fold cross validation method. Conclusion: In this study, we developed new prediction equations for the diffusing capacity of the lungs of Koreans. A further study is needed to validate the new predicting equation for diffusing capacity.
Baseflow gives a significant contribution to stream function in the regions where climatic characteristics are seasonally distinct. In this regard, variable baseflow can make it difficult to maintain a stable water supply, as well as causing disruption to the stream ecosystem. Changes in land use can affect both the direct flow and baseflow of a stream, and consequently, most other components of the hydrologic cycle. Baseflow estimation depends on the observed streamflow in gauge watersheds, but accurate predictions of streamflow through modeling can be useful in determining baseflow data for ungauged watersheds. Accordingly, the objectives of this study are to 1) improve predictions of SWAT by applying the alpha factor estimated using RECESS for calibration; 2) estimate baseflow in an ungauged watershed using the WHAT system; and 3) evaluate the effects of changes in land use on baseflow characteristics. These objectives were implemented in the Gapcheon watershed, as an ungauged watershed in South Korea. The results show that the alpha factor estimated using RECESS in SWAT calibration improves the prediction for streamflow, and, in particular, recessions in the baseflow. Also, the changes in land use in the Gapcheon watershed leads to no significant difference in annual baseflow between comparable periods, regardless of precipitation, but does lead to differences in the seasonal characteristics observed for the temporal distribution of baseflow. Therefore, the Guem River, into which the stream from the Gapcheon watershed flows, requires strategic seasonal variability predictions of baseflow due to changes in land use within the region.
Journal of the Korean Association of Geographic Information Studies
/
v.19
no.4
/
pp.17-35
/
2016
Earthquake-induced disasters are often more severe in locations with soft soils than firm soils or rocks due to differences in ground motion amplification. On a regional scale, such differences can be estimated by spatially predicting subsurface soil thickness over the entire target area. In general, soil deposits are generally deeper in coastal or riverside areas than in inland regions. In this study, a coastal metropolitan area, Incheon, was selected to assess site effects and provide information on seismic hazards. Spatial prediction of geotechnical layers was performed for the entire study area within the GIS framework. Approximately 7,000 existing borehole drilling data in the Incheon area were gathered and archived into the GIS Database (DB). In addition, surface geotechnical data were acquired from a walkover survey. Based on the built geotechnical DB, spatial zoning maps of site-specific seismic response parameters were created and presented for use in a regional seismic strategy. Site response parameters were performed to determine site coefficients for seismic design over the entire target area and compared with each other. Site classifications and subsequent seismic zoning were assigned based on site coefficients. From this seismic zonation case study in Incheon, we verified that geotechnical GIS-DB can create spatial zoning maps of site-specific seismic response parameters that are useful for seismic hazard mitigation particularly in coastal metropolitan areas.
The terrain features and surface characteristics are the most important elements not only in meteorological modeling but also in air quality modeling. The diurnal evolution of local climate over complex terrain may be significantly controlled by the ground irregularities. Such topographic features can affect a thermally driven flow, either directly by causing changes in the wind direction or indirectly, by inducing significant variations in the ground temperature. Over a complex terrain, these variations are due to the nonuniform distribution of solar radiation, which is highly determined by the ground geometrical characteristics, i.e. slope and orientation. Therefore, the accuracy of prediction of regional scale circulation is strong associated with the accuracy of land-use and topographic information in meso-scale circulation assessment. The objective of this work is a numerical simulation using MM5-A2C model with the detailed topography and land-use information as the surface boundary conditions of the air flow field in mountain regions. Meteorological conditions estimated by MM5-A2C command a great influence on the dispersion of mountain areas with the reasonable feature of topography where there is an important difference in orographic forcing.
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