• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1143-1154
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• 2021

The selection of activation function has a great influence on the groundwater level prediction performance of artificial neural network (ANN) model. In this study, five activation functions were applied to ANN model for two groundwater level observation wells in the middle mountainous area of the Pyoseon watershed in Jeju Island. The results of the prediction of the groundwater level were compared and analyzed, and the optimal activation function was derived. In addition, the results of LSTM model, which is a widely used recurrent neural network model, were compared and analyzed with the results of the ANN models with each activation function. As a result, ELU and Leaky ReLU functions were derived as the optimal activation functions for the prediction of the groundwater level for observation well with relatively large fluctuations in groundwater level and for observation well with relatively small fluctuations, respectively. On the other hand, sigmoid function had the lowest predictive performance among the five activation functions for training period, and produced inappropriate results in peak and lowest groundwater level prediction. The ANN-ELU and ANN-Leaky ReLU models showed groundwater level prediction performance comparable to that of the LSTM model, and thus had sufficient potential for application. The methods and results of this study can be usefully used in other studies.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.4
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• pp.33-49
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• 2019

The purpose of this study was to investigate the changes in the experiences of visitors of Yeonnam-dong during the period covering the development of a linear park, the Gyeongui Line Park. This study used a text mining technique to analyze Naver Blog postings of those who visited Yeonnam-dong from June 2013 to May 2017, divided into four periods -from June 2013 to May 2014, from June 2014 to May 2015, from June 2015 to May 2016 and from June 2016 to May 2017. The keywords used were 'Yeonnam-dong', 'Gyeongui Line' and 'Yeontral Park' and the data was further refined and resampled. A semantic network analysis was conducted on the basis of the co-occurrences of words. The results of the study were as follows. During the entire period, the main experience of visitors to Yeonnam-dong was 'food culture' consistently, but the activities related to 'market', 'browsing', and 'buy' increased. Also, activities such as 'walk', 'play' and 'rest' in the park newly appeared after the construction of the park. Moreover, more diverse opinions about the Yeonnam-dong were expressed on the blog, and Yeonnam-dong began to be recognized as a place where a variety of activities can be enjoyed. Lastly, when the visitors wrote about the theme 'food culture', the scope of the keywords expanded from simple ones, such as 'eat', 'photograph' and 'chatting' to 'market', 'browsing', and 'walk'. The sub-themes that appeared with the park also expanded to various topics with the emergence of the Gyeongui Line Book Street. This study analyzed the change of experiences of visitors objectively with text mining, a quantitative methodology. Due to the nature of text mining, however, the subjective opinions inevitably have been involved in the process of refining. Also, further research is required to assess the direct relationship between these changes and park construction.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.848-857
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• 2018

Recently, around the world, active development of new renewable energy sources including solar power, waves, and fuel cells, etc. has taken place. Particularly, floating offshore wind farms have been developed for saving costs through large scale production, using high-quality wind power and minimizing noise damage in the ocean area. The development of floating wind farms requires an evaluation of the Maritime Safety Audit Scheme under the Maritime Safety Act in Korea. Floating wind farms shall be assessed by applying the line and area concept for systematic development, management and utilization of specified sea water. The development of appropriate evaluation methods and standards is also required. In this study, proper standards for marine traffic surveys and assessments were established and a systemic treatment was studied for assessing marine spatial area. First, a marine traffic data collector using AIS or radar was designed to conduct marine traffic surveys. In addition, assessment methods were proposed such as historical tracks, traffic density and marine traffic pattern analysis applying the line and area concept. Marine traffic density can be evaluated by spatial and temporal means, with an adjusted grid-cell scale. Marine traffic pattern analysis was proposed for assessing ship movement patterns for transit or work in sea areas. Finally, conceptual design of a Marine Traffic and Safety Assessment Solution (MaTSAS) was competed that can be analyzed automatically to collect and assess the marine traffic data. It could be possible to minimize inaccurate estimation due to human errors such as data omission or misprints through automated and systematic collection, analysis and retrieval of marine traffic data. This study could provides reliable assessment results, reflecting the line and area concept, according to sea area usage.

• Korean Journal of Environment and Ecology
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• v.33 no.4
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• pp.378-401
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• 2019

This study generated a list of plants in eight sections from the Baekdansa ticket office (874m) to Cheonjedan (1,560m) divided in the interval of 100m above sea level to examine the species diversity patterns and distribution changes of the vascular plants at different altitudes in Taebaeksan National Park. Four site surveys found a total of 385 taxa: 89 families, 240 genera, 345 species, 5 subspecies, 34 varieties, and 1 form. A result of analyzing the change of species diversity along elevational gradients showed that it decreased with increasing elevation and then increased from a certain section. A result of analyzing habitat affinity types showed that the proportion of forest species increased with increasing elevation. On the other hand, the ruderal species appeared at a high rate in the artificial interference section. A result of comparing the proportion of woody and herb plants showed that the woody plants gradually increased with elevation and rapidly decreased in the artificial interference section. On the other hand, the herb plants showed the opposite trend. A result of analyzing the change of distribution of species according to altitude with the DCA technique showed that the vascular plants were divided into three groups according to the elevation in order on the I axis with the boundaries at 900m and 1,300m above sea level. The arrangement of each stand from right to left along the altitude on the I axis with a significant correlation with warmth index (WI) confirmed that the temperature change along the altitude could affect the distribution of vascular plants, composition, and diversity. Therefore, the continuous monitoring is necessary to confirm ecological and environmental characteristics of vegetation, distribution ranges, changes of habitat. We expect that the results of this study will be used as the basic data for establishing the measurement measures related to the preservation of biodiversity and climate change.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.51-78
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• 2021

Followed by 'start-up', the theme of 'scale-up' has been considered as an important agenda in both corporate and policy spheres. In particular, although it is a term commonly used in industry and policy fields, even a conceptual definition has not been achieved from the academic perspective. "Corporate Growth" in the academic aspect and "Business Growth" in the practical management field have different understandings (Achtenhagen et al., 2010). Previous research on corporate growth has not departed from Penrose(1959)'s "Firm as a bundle of resources" and "the role of managers". Based on the theory and background of economics, existing research has mainly examined factors that contribute to firms' growth and their growth patterns. Comparatively, we lack knowledge on the firms' growth with a focus on 'annual revenue growth rate'. In the early stage of the firms, they tend to exhibit a high growth rate as it started with a lower level of annual revenue. However, when the firms reach annual revenue of more than 100 billion KRW, a threshold to be classified as a 'middle-standing enterprise' by Korean standards, they are unlikely to reach a high level of revenue growth rate. In our study, we used our sample of 333 companies (6.7% out of 5,000 'fastest-growing' companies) which reached 15% of the compound annual growth rate in the last three years with more than USD 100 million. It shows that sustaining 'high-growth' above a certain firm size is difficult. The study focuses on firms with annual revenue of more than $100 billion (approximately 120 billion KRW) from the 'Inc. 2020 fast-growing companies 5,000' list. The companies have been categorized into 1) Fast-growing companies (revenue CAGR 15%~40% between 2016 and 2019), 2) Hyper-growing companies (40%~99.9%), and 3) Super-growing (100% or more) with in-depth analysis of each group's characteristics. Also, the relationship between the revenue growth rate, individual company's strategy choice (market orientation, generic strategy, growth strategy, pioneer strategy), industry/market environment, and firm age is investigated with a quantitative approach. Through conducting the study, it aims to provide a reference to the 'Hyper-Growing Model' that combines the paths and factors of growth strategies. For policymakers, our study intends to provide a reference to which factors or environmental variables should be considered for 'optimal effective combinations' to promote firms' growth.

• Korean Journal of Heritage: History & Science
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• v.54 no.1
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• pp.206-219
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• 2021

The warming of the climate is increasing the damage caused by termites to wooden buildings, cultural properties and houses. A group removal system can be installed around the building to detect and remove termite damage; however, if the site is not visited regularly, every one to two months, you cannot observe whether termites have spread within, and it is difficult to take prompt effective action. In addition, since the system is installed and operated in an exposed state for a long period of time, it may be ineffective or damaged, resulting in a loss of function. Furthermore if the system is installed near a cultural site, it may affect the aesthetic environment of the site. In this study, we created a detection system that uses wood, cellulose, magnets, and magnetic sensors to determine whether termites have entered the area. The data was then transferred to a low power LoRa Network which displayed the results without the necessity of visiting the site. The wood was made in the shape of a pile, and holes were made from the top to the bottom to make it easier for termites to enter and produce a cellulose sample. The cellulose sample was made in a cylindrical shape with a magnet wrapped in cellulose and inserted into the top of a hole in the wood. Then, the upper part of the wood pile was covered with a stopper to prevent foreign matter from entering. It also served to block external factors such as light and rainfall, and to create an environment where termites could add cellulose samples. When the cellulose was added by the termites, a space was created around the magnet, causing the magnet to either fall or tilt. The magnetic sensor inside the stopper was fixed on the top of the cellulose sample and measured the change in the distance between the magnet and the sensor according to the movement of the magnet. In outdoor experiments, 11 cellulose samples were inserted into the wood detection system and the termite inflow was confirmed through the movement of the magnet without visiting the site within 5 to 17 days. When making further improvements to the function and operation of the system it in the future, it is possible to confirm that termites have invaded without visiting the site. Then it is also possible to reduce damage and fruiting due to product exposure, and which would improve the condition and appearance of cultural properties.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.694-709
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• 2021

Purpose: In this study, earthquake-induced landslide risk assessment was conducted to provide basic data for efficient and preemptive damage prevention by selecting the erosion control work before the earthquake and the prediction and restoration priorities of the damaged area after the earthquake. Method: The study analyzed the previous studies abroad to examine the evaluation methodology and to derive the evaluation factors, and examine the utilization of the landslide hazard map currently used in Korea. In addition, the earthquake-induced landslide hazard map was also established on a pilot basis based on the fault zone and epicenter of Pohang using seismic attenuation. Result: The earthquake-induced landslide risk assessment study showed that China ranked 44%, Italy 16%, the U.S. 15%, Japan 10%, and Taiwan 8%. As for the evaluation method, the statistical model was the most common at 59%, and the physical model was found at 23%. The factors frequently used in the statistical model were altitude, distance from the fault, gradient, slope aspect, country rock, and topographic curvature. Since Korea's landslide hazard map reflects topography, geology, and forest floor conditions, it has been shown that it is reasonable to evaluate the risk of earthquake-induced landslides using it. As a result of evaluating the risk of landslides based on the fault zone and epicenter in the Pohang area, the risk grade was changed to reflect the impact of the earthquake. Conclusion: It is effective to use the landslide hazard map to evaluate the risk of earthquake-induced landslides at the regional scale. The risk map based on the fault zone is effective when used in the selection of a target site for preventive erosion control work to prevent damage from earthquake-induced landslides. In addition, the risk map based on the epicenter can be used for efficient follow-up management in order to prioritize damage prevention measures, such as to investigate the current status of landslide damage after an earthquake, or to restore the damaged area.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.163-173
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• 2022

Climate change is a key factor that greatly influences changes in the biological seasons and geographical distribution of species. In the ecological field, the BioClimatic predictor (BioClim), which is most related to the physiological characteristics of organisms, is used for vulnerability assessment. However, BioClim values are not provided other than the future period climate average values for each GCM for the Shared Socio-economic Pathways (SSPs) scenario. In this study, BioClim data suitable for domestic conditions was produced using 1 km resolution SSPs scenario detailed data produced by Rural Development Administration, and based on the data, a species distribution model was applied to mainly grow in southern, Gyeongsangbuk-do, Gangwon-do and humid regions. Appropriate habitat distributions were predicted every 30 years for the base years (1981 - 2010) and future years (2011 - 2100) of the Acer pictum subsp. mono. Acer pictum subsp. mono appearance data were collected from a total of 819 points through the national natural environment survey data. In order to improve the performance of the MaxEnt model, the parameters of the model (LQH-1.5) were optimized, and 7 detailed biolicm indices and 5 topographical indices were applied to the MaxEnt model. Drainage, Annual Precipitation (Bio12), and Slope significantly contributed to the distribution of Acer pictum subsp. mono in Korea. As a result of reflecting the growth characteristics that favor moist and fertile soil, the influence of climatic factors was not significant. Accordingly, in the base year, the suitable habitat for a high level of Acer pictum subsp. mono is 3.41% of the area of Korea, and in the near future (2011 - 2040) and far future (2071 - 2100), SSP1-2.6 accounts for 0.01% and 0.02%, gradually decreasing. However, in SSP5-8.5, it was 0.01% and 0.72%, respectively, showing a tendency to decrease in the near future compared to the base year, but to gradually increase toward the far future. This study confirms the future distribution of vegetation that is more easily adapted to climate change, and has significance as a basic study that can be used for future forest restoration of climate change-adapted species.