• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.203-215
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• 2017

Recently, interest in Pedestrian Navigation Service (PNS) is being increased due to the diffusion of smart phone and the improvement of location determination technology and it is efficient to use landmarks in route guidance for pedestrians due to the characteristics of pedestrians' movement and success rate of path finding. Accordingly, researches on extracting landmarks have been progressed. However, preceding researches have a limit that they only considered the difference between buildings and did not consider visual attention of maps in display of PNS. This study improves this problem by defining building attributes as local variable and global variable. Local variables reflect the saliency of buildings by representing the difference between buildings and global variables reflects the visual attention by representing the inherent characteristics of buildings. Also, this study considers the connectivity of network and solves the overlapping problem of landmark candidate groups by network voronoi diagram. To extract landmarks, we defined building attribute data based on preceding researches. Next, we selected a choice point for pedestrians in pedestrian network data, and determined landmark candidate groups at each choice point. Building attribute data were calculated in the extracted landmark candidate groups and finally landmarks were extracted by principal component analysis. We applied the proposed method to a part of Gwanak-gu, Seoul and this study evaluated the extracted landmarks by making a comparison with labels and landmarks used by portal sites such as the NAVER and the DAUM. In conclusion, 132 landmarks (60.3%) among 219 landmarks of the NAVER and the DAUM were extracted by the proposed method and we confirmed that 228 landmarks which there are not labels or landmarks in the NAVER and the DAUM were helpful to determine a change of direction in path finding of local level.

• 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.