• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.545-553
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• 2021

The frequency by water disaster in urban areas are increasing continuously due to climate change and urbanization. Countermeasures are being conducted to reduce the damage caused by water disasters. An analysis based on permeability, one of the parameters that affect runoff, is needed to predict quantitative runoff in urban watersheds and study runoff reduction. In this study, the SWAT model was simulated for the oncheon stream basin, a representative urban stream in Busan. The permeability map was prepared by calculating the CN values for each hydrologic response unit. Based on the permeability map prepared, EPA SWMM analyzed the effect of LID technology application on the water cycle in the basin for short-term rainfall events. The LID element technology applied to the oncheon stream basin was rooftop greening in the residential complex, and waterproof packaging was installed on the road. The land cover status of the land selected based on the permeability map and the application of LID technology reduced the outflow rate, peak flow rate, and outflow rate and increased the infiltration. Hence, LID technology has a positive effect on the water cycle in an urban basin.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.64-71
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• 2021

Recently, climate change has gradually accelerated the occurrence of landslides. Among the various effects caused by landslides,debris flow is recognized as particularly threatening because of its high speed and propagating distance. In this study, the impacts of various factors were analyzed using quantification theory(I) for the prediction of debris flow hazard soil volume in Seoraksan National Park, Korea. According to the range using the stepwise regression analysis, the order of impact factors was as follows: vertical slope (0.9676), cross slope (0.6876), altitude (0.2356), slope gradient (0.1590), and aspect (0.1364). The extent of the normalized score using the five-factor categories was 0 to 2.1864, with the median score being 1.0932. The prediction criteria for debris flow occurrence based on the normalized score were divided into four grades: class I, >1.6399; class II, 1.0932-1.6398; class III, 0.5466-1.0931; and class IV, <0.5465. Predictions of debris flow occurrence appeared to be relatively accurate (86.3%) for classes I and II. Therefore, the prediction criteria for debris flow will be useful for judging the dangerousness of slopes.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.867-874
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• 2023

Recently, many problems are occurring in the bee ecosystem due to rapid climate change. The decline in the bee population and changes in the flowering period are having a huge impact on the harvest of bee-keepers. Since it is impossible to continuously observe the beehives in the hive with the naked eye, most people rely on knowledge based on experience about the state of the hive.Therefore, interest is focused on smart beekeeping incorporating IoT technology. In particular, with regard to swarming, which is one of the most important parts of beekeeping, we know empirically that the swarming time can be determined by the sound of the queen bee, but there is no way to systematically analyze this with data.You may think that it can be done by simply recording the sound of the queen bee and analyzing it, but it does not solve various problems such as various noise issues around the hive and the inability to continuously record.In this study, we developed a system that records queen bee sounds in a real-time cloud system and analyzes sound patterns.After receiving real-time analog sound from the hive through multiple channels and converting it to digital, a sound pattern that was continuously output in the queen bee sound frequency band was discovered. By accessing the cloud system, you can monitor sounds around the hive, temperature/humidity inside the hive, weight, and internal movement data.The system developed in this study made it possible to analyze the sound patterns of the queen bee and learn about the situation inside the hive. Through this, it will be possible to predict the swarming period of bees or provide information to control the swarming period.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.41-57
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• 2023

The drybulk shipping market has high freight rate volatility in the chartering market and various and complex factors affecting the market. In the unstable economic situation caused by the COVID-19 pandemic in 2020, the BDI plunged due to a decrease in trade volume, but turned from the end of 2020 and maintained a booming period until the end of 2022. The main reason for the market change is the decrease in the available fleet that can actually be operated for cargo transport due to port congestion by the COVID-19 pandemic, regardless of the fleet and trade volume volatility that have affected the drybulk shipping market in the past. A decrease in the actual usable fleet due to vessel waiting at port by congestion led to freight increase, and the freight increase in charting market led to an increase in second-hand ship and new-building ship price in long-term equilibrium relationship. In the past, the drybulk shipping market was determined by the volatility of fleet and trade volume. but, in the future, available fleet volume volatility by pandemics, environmental regulations and climate will be the important factors affecting BDI. To response to the IMO carbon emission reduction in 2023, it is expected that ship speed will be slowed down and more ships are expected to be needed to transport the same trade volume. This slowdown is expected to have an impact on drybulk shipping market, such as a increase in freight and second-hand ship and new-building ship price due to a decrease in available fleet volume.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.80-96
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• 2023

The expansion of human activities and road development has led to the loss and fragmentation of ecological spaces, which is a negative factor for biodiversity. In particular, urban areas where land use and land cover have rapidly changed into urbanization zones are regions where ecological spaces are lost and isolated, making it difficult for wildlife to inhabit. Furthermore, the loss and fragmentation of ecological spaces due to urbanization can have a negative impact on ecosystem services. Therefore, to enhance biodiversity and ecosystem services in urban and national land, it is necessary to establish a practical ecological axis that reflects the current status of the city. Thus, this study analyzed the connectivity of ecological spaces and forest axis that can be used for spatial planning related to urban ecological axis of local governments in Daegu and Gyeongsangbuk-do. The ecological connectivity was analyzed by dividing the Daegu-Gyeongbuk region into 31 local government units, distinguishing between forests and natural areas using land cover data. Subsequently, the study area was divided into 20,483 hexagonal grids of 1 square kilometer each, and the restoration effects for ecological fragmentation within 100 meters were spatially clustered to visualize priority restoration areas. The forest axis was derived by considering regional conditions such as land cover, building area, slope, and others to connect 1,534 forests of 100 hectares or more. The research results are expected to be used as fundamental data for spatial planning, goal setting, and the selection of restoration areas for improving ecological connectivity.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.3
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• pp.18-32
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• 2024

This study aims to analyze the summer human thermal environment in Jeju City's outdoor parking lots by measuring microclimate data and comparing pavement and vegetation albedoes and elements through computer simulations. In measured cases, results due to albedo showed no significance, but there was a significant difference between sunny and shaded areas by trees. The sunny area had a PET (physiological equivalent temperature) in the 'very hot' level, while the shaded area exhibited a 2-step lower 'warm' level. UTCI (universal thermal climate index) also showed that the sunny area was in the 'very strong heat stress' level, whereas the shaded area was 1-step lower in the 'strong heat stress' level, confirming the role of trees in reducing incoming solar radiant energy. Simulation results, using the measured albedoes, closely resembled the measured results. Regarding vegetation, scenarios with a wide canopy, high leaf density, and narrow planting spacing were effective in mitigating the human thermal environment, and the differences due to tree height varied across scenarios. The scenario with the lowest PET value was H9W9L3D8 (tree height 9m, canopy width 9m, leaf area index 3.0, planting spacing 8m), indicating a 0.7-step decrease compared to the current landscaping scenario. Thus, it was confirmed that, among landscaping elements, trees have a significant impact on the summer human thermal environment compared to ground pavement.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.245-253
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• 2024

Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.3
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• pp.12-21
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• 2020

This study aims to implement the Sustainability Evaluation Framework of Korea Natural Parks to county parks in Gyeongsangnam-do, and to review the performance status of management effectiveness evaluation (MEE) and identify factors that influence the improvement of management effectiveness in protected areas. County park officers evaluated current management using this framework that was developed based on the MEE framework designed by the Korean Ministry of Environment. Among the principal values of county parks, 'natural and ecological' is indicated as the most important, followed by 'cultural and historic value' and 'leisure and recreation'. Natural disasters and climate change, visitor impact-inappropriate visitor behavior are indicated as current threats, and three county parks administrators viewed that there was no particular threat to their park. According to MEE results, the most effective management fields were 'State of cultural and historic value', 'State of leisure and recreational value', 'Current state of principal value'. The comparatively weaker fields were 'Threatened species management', 'Invasive species management', 'Management monitoring and evaluation'. The effects of sustainable management on county parks were analyzed through a regression analysis, and the influence of management factors reveal 'Annual budget', will impact attaining higher management scores. This study presents the current management information about county parks and provides support for the basis for the planning of county parks in Korea, suggesting the influencing factor.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.