• Food Science of Animal Resources
• /
• v.32 no.1
• /
• pp.118-124
• /
• 2012

This study was conducted for quantitative microbial risk assessment (QMRA) of Clostridium perfringens with consumption on ham and sausage products in Korea, according to Codex guidelines. Frame-work model as product-retail-consumption pathway composed with initial contamination level, the time and temperature in distributions, and consumption data sets for ham and sausage products and also used the published predictive growth and dose-response models for Cl. perfringens. The simulation model and formulas with Microsoft@ Excel spreadsheet program using these data sets was developed and simulated with @RISK. The probability of foodborne disease by Cl. perfringens with consumption of the ham and sausage products per person per day was estimated as $3.97{\times}10^{-11}{\pm}1.80{\times}10^{-9}$. There were also noted that limitations in this study and suggestion for development of QMRA in the future in Korea.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.136-136
• /
• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Journal of Korea Water Resources Association
• /
• v.36 no.4
• /
• pp.691-703
• /
• 2003

In this paper, a new and integrated approach easily used to calculate the pollutant loads from agricultural watersheds is suggested. Basic concepts of this empirical tool are based on the hypotheses that variations in event mean concentrations(EMCs) of the pollutants from a given agricultural watershed during rainstorms are only due to the rainfall pattern. This assumption would be feasible to agricultural watersheds whose land uses does not change during the cultivation period overlapped by rainy season and also in which point-sources of the pollutants are rare. Therefore, if EMC data sets through extensive sampling from various rural areas are available, it is possible to establish relationships between EMCs, shapes and land uses of the watersheds, and rainfall events. For this purpose, fifty one sets of EMC values were obtained from nine different watersheds, and those data were used to develop predictive tools for the EMCs of 55, COD, TN and TP in rainfall runoff. The results of the statistical tests for those formulas show that they are not only fairly good in predicting actual EMC values of some parameters, but also useful in terms of calculating pollutant loads on any time-spans such as the day of rainfall event or weekly, monthly, and yearly. Their applicability was briefly demonstrated and discussed. Also, the unit loads calculated from EMCs based on different land uses and real rainfall data over one of the watershed used for this study. were provided, and they are compared with other well-known unit loads.