• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.845-859
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• 2020

Climate change and recent heat waves have drawn public attention toward other environmental issues, such as water pollution in the form of algal blooms, chemical leaks, and oil spills. Water pollution by the leakage of chemicals may severely affect human health as well as contaminate the air, water, and soil and cause discoloration or death of crops that come in contact with these chemicals. Chemicals that may spill into water streams are often colorless and water-soluble, which makes it difficult to determine whether the water is polluted using the naked eye. When a chemical spill occurs, it is usually detected through a simple contact detection device by installing sensors at locations where leakage is likely to occur. The drawback with the approach using contact detection sensors is that it relies heavily on the skill of field workers. Moreover, these sensors are installed at a limited number of locations, so spill detection is not possible in areas where they are not installed. Recently hyperspectral images have been used to identify land cover and vegetation and to determine water quality by analyzing the inherent spectral characteristics of these materials. While hyperspectral sensors can potentially be used to detect chemical substances, there is currently a lack of research on the detection of chemicals in water streams using hyperspectral sensors. Therefore, this study utilized remote sensing techniques and the latest sensor technology to overcome the limitations of contact detection technology in detecting the leakage of hazardous chemical into aquatic systems. In this study, we aimed to determine whether 18 types of hazardous chemicals could be individually classified using hyperspectral image. To this end, we obtained hyperspectral images of each chemical to establish a spectral library. We expect that future studies will expand the spectral library database for hazardous chemicals and that verification of its application in water streams will be conducted so that it can be applied to real-time monitoring to facilitate rapid detection and response when a chemical spill has occurred.

• Journal of the Korean Geotechnical Society
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• v.35 no.11
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• pp.75-95
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• 2019

The numerical analysis on PHC piles socketed into weathered rocks through sandy soil layers was conducted to propose the table solution or the chart solution to obtain the mobilization capacity. The mobilization capacity was determined at the settlement of 5% pile diameter and applied a safety factor of 3.0. In order to utilize the excellent compressive strength of the PHC pile effectively, it is recommended that the allowable bearing capacity of ground would be designed to be more than the long-term allowable compressive pile load. A procedure for determining an allowable pile capacity for PHC piles socketed into weathered rocks through sandy soil layers is given by the sum of the allowable skin friction of the sandy soil layer and the weathered rock layer and the allowable end bearing capacity of the weathered rock layer. The design efficiency of the PHC pile is about 85% at the reasonable design stage in the verification of the newly proposed method. Thus, long-term allowable compressive load (P_all) level of PHC piles can be utilized in the optimal design stage.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.315-325
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• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.394-404
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• 2013

In the present study, the characteristics of spectral peakedness parameter $Q_p$, bandwidth parameter ${\varepsilon}$, and spectral width parameter ${\nu}$ were analyzed as a first step to define the swell waves quantitatively. For the analysis, the joint probability density function of significant wave heights and peak periods were newly developed. The MCMC(Markov Chain Monte Carlo) simulations have been performed to generate the significant wave heights and peak periods from the developed probability density functions. Applying the simulated significant wave heights and peak periods to the theoretical wave spectrum models, the spectral shapes parameters were obtained and analyzed. Among the spectral shape parameters, only the spectral peakedness parameter $Q_p$, is shown to be independent with the significant wave height and peak wave period. It also best represents the peakedness of the spectral shape, and henceforth $Q_p$ should be used to define the swell waves with a wave period. For the field verification of the results, wave data obtained from Hupo port and Ulleungdo were analyzed and results showed the same trend with the MCMC simulation results.

• Journal of the Korean Institute of Gas
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• v.21 no.6
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• pp.88-95
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• 2017

The usage of toxic gas in Korea is increasing in the development of high-tech industries such as semiconductors, displays and solar panels. The recent survey of domestic toxic gas consumption indicates an increase in annual average of 12.4 percent, but it is still focused on usage, and it is negligent in safety and treating the post. In September 2012, an accident occurred in Gu-mi involving hydrofluoric acid leak demonstrates the absence of safety management. Due to the incident, the government, industry and academia have been interested in chemical substances(toxic gas), and the government-led safety management has been established and implemented, but there are still a lot of safety blind spots. The purpose of this study is to develop effective measurement methods for the destruction or removal efficiency of gaseous materials emitted from the Scrubber used in the semiconductor and display industries. Also, this study demonstrated how toxic gas facilities can be applied without error by verification test for the measurement method guideline of the destruction or removal efficiency of the green-house gas reduction facility in the semiconductor and display industries used by the National Institute of Environmental Research and the UNFCCC, and suggested the differentiated measurement methods for toxic gas reduction facilities, and the third party certification for safety facilities is needed to prevent toxic gas accidents.

• Journal of the Korean Society of Floral Art and Design
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• no.37
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• pp.33-48
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• 2017

This study examines garden activity and garden plant preferences for development of the garden activity program using community garden aimed for communication and integration for multicultural age. As a result, for members of multicultural society had high portion of floral arrangement and crafts, growing plants in both indoors and outdoors garden for their garden activities, and using plants for cooking, growing plants in both indoors and outdoors garden was of higher portion for native korean. In the garden plant preferences, members of multicultural society liked ornamental plants the best, due to the environmental correspondence between the plant's place of origin and their home country, while native koreans tend to prefer vegetables, reflecting the recent interest in pro-environment crops and rise in demand of urban farming, veranda gardening and weekend farming. In this study, the garden activity program for communication and integration categorized the value of garden activity into three categories; the value of respect for life, the value of consideration through caring, the value of plant ethics, based on the above preference results. The value of respect for life can be achieved by understanding the meaning of life, experiencing the will to live, and understanding the characteristics of plants and me. The value of consideration of caring comes from waiting and nurturing for living things that are different from me and adapting to the environment as a living The value of plant ethics can give us the insights for human relationships, by understanding and experiencing the natural ecosystem and plant co-existing in it. The eight-session garden activity program also went through validity verification process by experts on gardening and multiculture, and the effectiveness of the program was proved.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.249-261
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• 2020

Coastal monitoring using multiple platforms/sensors is a very important tools for accurately understanding the changes in offshore marine environment and disaster with high temporal and spatial resolutions. However, integrated observation studies using multiple platforms and sensors are insufficient, and none of them have been evaluated for efficiency and limitation of convergence. In this study, we aimed to suggest an integrated observation method with multi-remote sensing platform and sensors, and to diagnose the utility and limitation. Integrated in situ surveys were conducted using Rhodamine WT fluorescent dye to simulate various marine disasters. In September 2019, the distribution and movement of RWT dye patches were detected using satellite (Kompsat-2/3/3A, Landsat-8 OLI, Sentinel-3 OLCI and GOCI), unmanned aircraft (Mavic 2 pro and Inspire 2), and manned aircraft platforms after injecting fluorescent dye into the waters of the South Sea-Yeosu Sea. The initial patch size of the RWT dye was 2,600 ㎡ and spread to 62,000 ㎡ about 138 minutes later. The RWT patches gradually moved southwestward from the point where they were first released,similar to the pattern of tidal current flowing southwest as the tides gradually decreased. Unmanned Aerial Vehicles (UAVs) image showed highest resolution in terms of spatial and time resolution, but the coverage area was the narrowest. In the case of satellite images, the coverage area was wide, but there were some limitations compared to other platforms in terms of operability due to the long cycle of revisiting. For Sentinel-3 OLCI and GOCI, the spectral resolution and signal-to-noise ratio (SNR) were the highest, but small fluorescent dye detection was limited in terms of spatial resolution. In the case of hyperspectral sensor mounted on manned aircraft, the spectral resolution was the highest, but this was also somewhat limited in terms of operability. From this simulation approach, multi-platform integrated observation was able to confirm that time,space and spectral resolution could be significantly improved. In the future, if this study results are linked to coastal numerical models, it will be possible to predict the transport and diffusion of contaminants, and it is expected that it can contribute to improving model accuracy by using them as input and verification data of the numerical models.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.6
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• pp.421-428
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• 2014

For the analysis of water supply network, demand-driven and pressure-driven analysis methods have been proposed. Of the two methods, demand-driven analysis (DDA) can only be used in a normal operation condition to evaluate hydraulic status of a pipe network. Under abnormal conditions, i.e., unexpected pipe destruction, or abnormal low pressure conditions, pressure-driven analysis (PDA) method should be used to estimate the suppliable flowrate at each node in a network. In order to carry out the pressure-driven analysis, head-outflow relationship (HOR), which estimates flowrate at a certain pressure at each node, should be first determined. Most previous studies empirically suggested that each node possesses its own characteristic head-outflow relationship, which, therefore, requires verification by using actual field data for proper application in PDA modeling. In this study, a model pipe network was constructed, and various operation scenarios of normal and abnormal conditions, which cannot be realized in real pipe networks, were established. Using the model network, data on pressure and flowrate at each node were obtained at each operation condition. Using the data obtained, previously proposed HOR equations were evaluated. In addition, head-outflow relationship at each node was analyzed especially under multiple pipe destruction events. By analyzing the experimental data obtained from the model network, it was found that flowrate reduction corresponding to a certain pressure drop (by pipe destruction at one or multiple points on the network) followed intrinsic head-outflow relationship of each node. By comparing the experimentally obtained head-outflow relationship with various HOR equations proposed by previous studies, the one proposed by Wagner et al. showed the best agreement with the exponential parameter, m of 3.0.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.