• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Science Education
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• v.33 no.1
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• pp.12-30
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• 2009

The purpose of this study was to analyze recognition characteristics of science gifted students on the earth system based on their thinking style. The subjects were 24 science gifted students at the Science Institute for Gifted Students of a university located in metropolitan city in Korea. The students' thinking styles were firstly examined on the basis of the Sternberg's theory of mental self-government. And then, the students were divided into two groups: Type I group(legislative, judicial, global, liberal) and Type II group(executive, local, conservative) based on Sternberg's theory. Data was collected from three different type of questionnaires(A, B, C types), interview, word association method, drawing analyses, concept map, hidden dimension inventory, and in-depth interviews. The findings of analysis indicated that their thinking styles were characterized by 'Legislative', 'Executive', 'Anarchic', 'Global', 'External', 'Liberal' styles. Their preference were conducting new projects and using creative problem solving processes. The results of students' recognition characteristics on earth system were as follows: First, though the two groups' quantitative value on 'System Understanding' was very similar, there were considerable distinctions in details. Second, 'Understanding the Relationship in the System' was closely connected to thinking styles. Type I group was more advantageous with multiple, dynamic, and recursive approach. Third, in the relation to 'System Generalization' both of the groups had similar simple interpretational ability of the system, but Type I group was better on generalization when 'hidden dimension inventory' factor was added. On the system prediction factor, however, students' ability was weak regardless of the type. Consequently, more specific development strategies on various objects are needed for the development and application of the system learning program. Furthermore, it is expected that this study could be practically and effectively used on various fields related to system recognition.