• Journal of Korean Society of Forest Science
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• v.81 no.1
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• pp.53-65
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• 1992

For elucidating effective methods of restoration of forest recreational sites where management goals are maintaining naturalness and conserving natural ecosystem, seeding, fertilization and soil surface treatment were used for four years at the devastated forest-floor. For restoration of forest-floor vegetation, factorial experiment was used with a split plot design(main plot : fertilization, subplot : soil surface${\times}$seeding) and a randomized complete block design (fertilization${\times}$seeding) at the Kwanaksan Aboretum, Anyang, Kyonggido. Results were summarized as follows : Soil surface softening with tipping and ripping and straw-mat mulching (70% coverage) treatment was effective on germination, survival and growth of seeded vegetation at devastated forest-floor. Especially, straw-mat mulching treatment was effective on soil surface stabilization and seedling's survival at eroded soil surface, while complete soil surface softening treatment was effective on germination, survival and early growth of tree species of late-successional series. Introducing seeds of native species of pioneer or early-successional series, with good growth capability in barren soil was effective on rapid restoration in devastated forest-floor with its soil surface previously compacted and its surviving seeds washed away. When the seeding and straw-mat mulching after partial soil surface softening with tipping and ripping treatment were employed, it took about three years to restore the devastated forest-floor where surface erosion had been undertaken for an extended period of time and where naturally surviving seeds of native species had been washed away. Softening treatment of soil surface was effective for about two years, and seeding and soil surface treatment increased number of seedlings and improved soil surface environment through fixing of movement of the fallen leaves. Fertilizing effect was not oberserved, mainly due to seeding exposure and poor physical condition including soil surface erosion, low soil water potential and drought, etc, at the field experimental site. However, application of nitrogen and phosphate fertilizers was effective on seedling survival of the species in late-successional series, while lime application adversely affected the seedling survival.

• 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.