• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.5
• /
• pp.1443-1454
• /
• 2014

In this study, we assess impact of spatial resolution of radar rainfall and a distributed hydrologic model on parameter estimation and rainfall-runoff response. Radar data measured by S-band polarimetric radar located at Mt. Bisl in the year of 2012 are used for the comparative study. As different rainfall estimates such as R-KDP, R-Z, and R-ZDR show good agreement with ground rainfall, R-KDP are applied for rainfall-runoff modeling due to relatively high accuracy in terms of catchment averaged and gauging point rainfall. GRM (grid based rainfall-runoff model) is implemented for flood simulations at the Geumho River catchment with spatial resolutions of 200m, 500m, and 1000m. Automatic calibration is performed by PEST (model independent parameter estimation tool) to find suitable parameters for each spatial resolution. For 200m resolution, multipliers of overlandflow and soil hydraulic conductivity are estimated within stable ranges, while high variations are found from results for 500m and 1000m resolution. No tendency is found in the estimated initial soil moisture. When parameters estimated for different spatial resolution are applied for other resolutions, 200m resolution model shows higher sensitivity compared to 1000m resolution model.

• Journal of the Korean Society for Library and Information Science
• /
• v.45 no.3
• /
• pp.327-343
• /
• 2011

The goal of this study was to examine ways to improve the quality of databases of the Korean National Assembly Digital Library(KNADL). In order to achieve the goal, we first designed an assessment tool that could measure the quality of the 'contents,' the 'service,' and the 'system' of the KNADL's databases. Each of the three categories also had sub-categories-i.e., eight sub-categories for 'contents'(e.g., accuracy, recency, ${\cdots}$), seven sub-categories for 'service'(e.g., convenience for request, rapid response, ${\cdots}$), and seven sub-categories for 'system'(e.g., usability, response time, ${\cdots}$). We thenconducted a survey using the assessment tool we developed and gathered a total of 270 responses from users of KNADL's databases. We used Excel and PASW Statistics 18 for data analysis. Each sub-category was measured by its importance and by the level of satisfaction(implemented from the DigiQUAL project). Finally, we performed an importance-satisfaction analysis(ISA) to identify what action(i.e., maintain, concentrate, low priority, and exceed) needs to be made in each sub-category. We concluded the paper with some useful suggestions for improving the quality of KNADL's databases.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.13 no.1 s.53
• /
• pp.88-96
• /
• 2009

This study proposes a wireless measurement system that is a new safety management system by using an FBG sensor and a PDA. The sensor part has many advantages of implementing a wireless measurement system, and the study emploies an FBG-LVDT sensor, FBG-STRAIN sensor, FBG-TEMP sensor, and FBG-ACC sensor, using FBG sensors. Also, the study show a configuration of a signal process system for operating a wireless transmission system of FBG sensors applied to the signal process system, and engrafted the cutting edge information technology industry in order to display from a remote distance using a PDA. In order to verify the applicability of the developed FBG sensors and wireless measurement monitoring system to the field, their accuracy, and usability, the study has conducted a static and dynamic test to a bridge in the field. The study made an assessment of service for the vibration of the bridge by applying dynamic data measured by an FBG-LVDT sensor and FBG-ACC sensor to Meister's curve and prepared methods for assessing the vibration of the bridge by proposing a standard of vibration limitation given the service of vibration of the bridge. As a follow up for this study, it would be necessary to set up an overall model for the standard of service assessment established in this study.

• Journal of Digital Convergence
• /
• v.15 no.2
• /
• pp.121-133
• /
• 2017

The propose of study is to examine the characteristics of companies with high possibility to form an internal control weakness using forecasting model. This study use the actual listed/unlisted companies' data from K_financial institution. The first conclusion is that discriminant model is more valid than logit model to predict internal control weak companies. A discriminant model for predicting the vulnerability of internal control has high classification accuracy and has low the Type II error that is incorrectly classifying vulnerable companies to normal companies. The second conclusion is that the characteristic of weak internal control companies have a low credit rating, low asset soundness assessment, high delinquency rates, lower operating cash flow, high debt ratios, and minus operating profit to the net sales ratio. As not only a case of listed companies but unlisted companies which did not occur in previous studies are extended in this study, research results including the forecasting model can be used as a predictive tool of financial institutions predicting companies with high potential internal control weakness to prevent asset losses.

• Korean Journal of Remote Sensing
• /
• v.33 no.6_2
• /
• pp.1047-1060
• /
• 2017

In recent years, global warming has been continuing and abnormal weather phenomena are occurring frequently. Especially in the 21st century, the intensity and frequency of hydrological disasters are increasing due to the regional trend of water. Since the damage caused by disasters in urban areas is likely to be extreme, it is necessary to prepare a landslide susceptibility maps to predict and prepare the future damage. Therefore, in this study, we analyzed the landslide vulnerability using the logistic model and assessed the management plan after the landslide through the field survey. The landslide area was extracted from aerial photographs and interpretation of the field survey data at the time of the landslides by local government. Landslide-related factors were extracted topographical maps generated from aerial photographs and forest map. Logistic regression (LR) model has been used to identify areas where landslides are likely to occur in geographic information systems (GIS). A landslide susceptibility map was constructed by applying a LR model to a spatial database constructed through a total of 13 factors affecting landslides. The validation accuracy of 77.79% was derived by using the receiver operating characteristic (ROC) curve for the logistic model. In addition, a field investigation was performed to validate how landslides were managed after the landslide. The results of this study can provide a scientific basis for urban governments for policy recommendations on urban landslide management.

• Journal of Korea Water Resources Association
• /
• v.56 no.9
• /
• pp.529-541
• /
• 2023

Due to the growing concern over forecasting extreme weather events such as droughts caused by climate change, there has been a rising interest in seasonal meteorological forecasts that offer ensemble predictions for the upcoming seven months. Nonetheless, limited research has been conducted in South Korea, particularly in assessing their effectiveness at the catchment-scale. In this study, we assessed the accuracy of ECMWF's seasonal forecasts (including precipitation, temperature, and evapotranspiration) for the period of 2011 to 2020. We focused on 12 multi-purpose reservoir catchments and compared the forecasts to climatology data. Continuous Ranked Probability Skill Score method is adopted to assess the forecast skill, and the linear scaling method was applied to evaluate its impact. The results showed that while the seasonal meteorological forecasts have similar skill to climatology for one month ahead, the skill decreased significantly as the forecast lead time increased. Compared to the climatology, better results were obtained in the Wet season than the Dry season. In particular, during the Wet seasons of the dry years (2015, 2017), the seasonal meteorological forecasts showed the highest skill for all lead times.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.781-791
• /
• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• The Journal of Engineering Geology
• /
• v.23 no.2
• /
• pp.137-147
• /
• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.4
• /
• pp.1-14
• /
• 2012

A river is defined as the watercourse flowing through its channel, and the mapping tasks of a river plays an important role for the research on the topographic changes in the riparian zones and the research on the monitoring of flooding in its floodplain. However, the utilization of the ground surveying technologies is not efficient for the mapping tasks of a river due to the irregular surfaces of the riparian zones and the dynamic changes of water level of a river. Recently, the spatial information data sets are widely used for the coastal mapping tasks due to the acquisition of the topographic information without human accessibility. In this research, we tried to extract a river from the RapidEye imagery by using the ISODATA(Iterative Self_Organizing Data Analysis) classification algorithm with the two different parameters(NIR (Near Infra-Red) band and NDVI(Normalized Difference Vegetation Index)). First, the two different images(the NIR band image and the NDVI image) were generated from the RapidEye imagery. Second, the ISODATA algorithm were applied to each image and each river was generated in each image through the post-processing steps. River boundaries were also extracted from each classified image using the Sobel edge detection algorithm. Ground truths determined by the experienced expert are used for the assessment of the accuracy of an each generated river. Statistical results show that the extracted river using the NIR band has higher accuracies than the extracted river using the NDVI.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.2
• /
• pp.83-94
• /
• 2015

Recently, the necessity of spatial data in unaccessible area was challenged to set up various plans and policies for preparing the unification and the cooperative projects between South-North Korea. Therefore, this paper planned to evaluate the possibility of adopting the error tolerance in Geometric correction for 1/5,000 digital topographic mapping, using the PLEIADES images and the TerraSAR GCPs (Ground Control Points). The geometric correction was performed by changing the number and placement of GCPs by GPS (Global Positioning System) surveying, as the optimal placement of 5 GCPs were selected considering the geometric stability and steady rate. The positional accuracy evaluated by the TerraSAR GCPs, which were selected by optimal placement of GCPs. The RMSE in control points were X=±0.64m, Y=±0.46m, Z=±0.28m. While the result of geometric correction for PLEIADES images confirmed that the RMSE in control points were X=±0.34m, Y=±0.27m, Z=±0.11m, the RMSE in check points were X=±0.50m, Y=±0.30m, Z=±0.66m. Through this study, we believe if spatial data can integrate with the PLEIADES images and the optimal TerraSAR GCPs, it will be able to obtain the high-precision spatial data for adopting the regulation of 1/5,000 digital topographic map, which adjusts the computation as well as the error bound.