• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of Forest and Environmental Science
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• v.38 no.3
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• pp.184-194
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• 2022

Triplochiton scleroxylon K. Schum is the plant species most affected by logging activities in the East Region of Cameroon due to its market value. This logging has impacted the ecological niche of the fern plant for which limited research has been done. The aim of this study is to contribute towards improving knowledge of fern richness and biomass on T. scleroxylon within the Central African sub-region. Fern data collection was done on 20 felled/harvested T. scleroxylon where, in addition to fern inventory, fern biomass was collected by the destructive method. The diameter and height of T. scleroxylon measured were used as explanatory variables in allometric equations for fern biomass estimation. Fern inventory was characterized using diversity index. Eight fern species were recorded on T. scleroxylon (≈5 species/T. scleroxylon). The minimum diameter where fern could be found is 59.4 cm. The average fern biomass found was 23.62 kg/T. scleroxylon. Pearson correlation coefficient showed a positive correlation (r>0.55) between fern biomass and T. scleroxylon diameter. For allometric equation, the logarithmic model improved better the adjustment than the non-logarithmic model. However, the quality of the adjustment is improved more when only the diameter is considered as an explanatory variable. Fern biomass is estimated to 90.08 kg/ha^-1 with 76.02 kg/ha^-1 being lost due to T. scleroxylon exploitation in the study area. This study is a contribution towards increasing knowledge of fern diversity specific to T. scleroxylon, and also fern biomass contribution to climate change mitigation and the potential carbon loss due to T. scleroxylon exploitation.

• Journal of Korea Water Resources Association
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• v.55 no.2
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• pp.121-133
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• 2022

The drought resulting from insufficient rainfall compared to the amount in an ordinary year can significantly impact a broad area at the same time. Another feature of this disaster is hard to recognize its onset and disappearance. Therefore, a reliable and fast way of predicting both the suffering area and the amount of water shortage from the upcoming drought is a key issue to develop a countermeasure of the disaster. However, the available drought scenarios are about 50 events that have been observed in the past. Due to the limited number of events, it is difficult to predict the water shortage in a case where the pattern of a natural disaster is different from the one in the past. To overcome the limitation, in this study, we applied the four RCP climate change scenarios to the water balance model and the annual amount of water shortage from 360 drought events was estimated. In the following chapter, the deep neural network model was trained with the SPEI values from the RCP scenarios and the amount of water shortage as the input and output, respectively. The trained model in each sub-basin enables us to easily and reliably predict the water shortage with the SPEI values in the past and the predicted meteorological conditions in the upcoming season. It can be helpful for decision-makers to respond to future droughts before their onset.

• Journal of Korean Society of Disaster and Security
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• v.15 no.3
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• pp.41-46
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• 2022

Recently, the scale of flood damage occurring in urban areas is increasing due to climate change and urbanization, so various flooding analysis techniques are needed. In the Sadangcheon Stream basin, which has been continuously flooded since 2010, a basic plan for improving drainage was established using XP-SWMM and measures to prevent flooding were proposed. However, in the process of inundation analysis, the analysis considering the city's buildings was not conducted, resulting in a problem that the degree of flooding damage tends to be overestimated. Therefore, in this study, XP-SWMM was used to compare and analyze cases where buildings were not considered and designated as inactive areas. As a result of the study, it was analyzed if the building was not considered, the flood damaged area was 271,100 m² and the depth of submersion was 0.15 m, and if the building was considered inactive area, the flood damaged area was 172,900 m² and the depth of submersion was 0.32 m that it is under-estimated about 36% and an flow velocity around the building increased from 1.62 m/s to 1.83 m/s about 1.12 times.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.335-345
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• 2022

Climate change is accelerating worldwide due to the recent rise in global temperature, and the intensity of typhoons is increasing due to the rise in seawater temperature around the Korean Peninsula. An increase in typhoon intensity is expected to increase not only wind damage, but also coastal damage caused by storm surge. Accordingly, in this study, a study of the method of reducing storm surges was conducted for the purpose of disaster prevention in order to respond to the increasing damage from storm surges. Storm surges caused by typhoons can be expected to be affected by structures located on the track of typhoon, and the effects of storm surges were studied by the eastern coast and the barrier island along the coast of the Gulf of Mexico in the United States. This study focused on this aspect and conducted related research, considering that storm surges in the southern coastal area of the Korean Peninsula could be directly or indirectly affected by Jeju Island, which is located on the track of typhoon. In order to analyze the impact of Jeju Island on storm surges, simulations were performed in various situations using a numerical analysis model. The results of using Jeju Island are thought to be able to be used to study new disaster prevention structures that respond to super typhoons.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.42-54
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• 2023

The population is concentrated in the metropolitan areas in Korea, and low-density residential areas are transforming into high density residential areas through redevelopment to meet this demand. However, large-scale redevelopment in a short period of time has a negative impact on the urban climate, such as generating a heat island effect due to the reduction of urban green areas. In this study, the change in surface temperature from 2013 to 2022 in the redevelpment areas of Godeok-dong and Dunchon-dong, Gangdong-gu, Seoul, was analyzed using Landsat 8 satellite images. In the Godeok-dong area, the difference in surface temperature was analyzed for the target redevelopment area, forest area, mixed forest and urban area, and low density residential area. In the Dunchon-dong area, the difference in surface temperature was analyzed for the target redevelopment area, forest area, and low density residential area. The difference in surface temperature was analyzed through multiple regression analysis conducted yearly over the three different stages in redevelopment period. The results from the multiple regression analysis show that in both areas, the land surface temperature of target redevelopment area was higher than that of the forest area and lower than low density residential area. It can be seen that these results occurred because the low-density residential area in Godeok-dong and Dunchon-dong had a lower green area ratio and a higher building-to-land ratio than the target redevelopment area. The results of this study suggest that even if low-density residential areas are transforming into high-density areas, adjusting the management of green areas and building-to-land ratio can contribute to lessen urban heat island effect.

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

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.193-205
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• 2009

Typhoons occurred in the tropical Pacific region, these might be affected the Korea moving toward north. The strong winds and the heavy rains by the typhoons caused a natural disaster in Korea. In the research, the heavy rainfall events based on typhoons were evaluated quantitative through various statistical techniques. First, probability precipitation and typhoon probability precipitation were compared using frequency analysis. Second, EST probability precipitation was calculated by Empirical Simulation Techniques (EST). Third, NL probability precipitation was estimated by coupled Nonparametric monte carlo simulation and Locally weighted polynomial regression. At the analysis results, the typhoons can be effected Gangneung and Mokpo stations more than other stations. Conversely, the typhoons can be effected Seoul and Inchen stations less than other stations. Also, EST and NL probability precipitation were estimated by the long-term simulation using observed data. Consequently, major hydrologic structures and regions where received the big typhoons impact should be review necessary. Also, EST and NL techniques can be used for climate change by the global warming. Because, these techniques used the relationship between the heavy rainfall events and the typhoons characteristics.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.