• Atmosphere
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• v.29 no.1
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• pp.53-59
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• 2019

The quality of troposphere ozone in three reanalysis datasets is evaluated with longterm ozonesonde measurement at Pohang, South Korea. The Monitoring Atmospheric Composition and Climate (MACC), European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERAI) and Modern Era Retrospective-Analysis for Research and Applications version 2 (MERRA2) are particularly examined in terms of the vertical ozone structure, seasonality and long-term trend in the lower troposphere. It turns out that MACC shows the smallest biases in the ozone profile, and has realistic seasonality of lower-tropospheric ozone concentration with a maximum ozone mixing ratio in spring and early summer and minimum in winter. MERRA2 also shows reasonably small biases. However, ERAI exhibits significant biases with substantially lower ozone mixing ratio in most seasons, except in mid summer, than the observation. It even fails to reproduce the seasonal cycle of lower-tropospheric ozone concentration. This result suggests that great caution is needed when analyzing tropospheric ozone using ERAI data. It is further found that, although not statistically significant, all datasets consistently show a decreasing trend of 850-hPa ozone concentration since 2003 as in the observation.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.578-578
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• 2016

In the past decade, many countries developed varies promising theories, methodologies and technologies for water resources management, such as Smart Water in Korea, eWater in Australia, Intelligent Water in Untied States, and Internet of Water in China. It is no exaggeration to say that Smart Water Management(SWM) will have a major role to play in addressing the global water challenges in the background of climate change, population growth and rapid urbanization. As a result, we can see major shifts taking place in the structure of the water industry, with a need for new approaches, skills, and water management policies. All these point towards a brighter future for the smart water sector and a new water paradigm, with applications and potential throughout the water cycle. However, each countries have their technology and industry standard system which may swift similar innovation and technology into different channels. In that sense, developing a common performance index and standard docking adapter for assessing Smart Water Management Initiatives(SWMI) is crucial for drawing a linkage of SWMI and SWMs to a way to implement advanced technology across Asia and Pacific. The performance index and standard docking adapter will facilitate quantitative and qualitative effects of utilized SWM techniques.

• Journal of Soil and Groundwater Environment
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• v.27 no.1
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• pp.50-59
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• 2022

There are several methods for separating the baseflow from the hydrograph, and graphical methods (GM) have mostly been used. GMs are those that separate the baseflow from the direct flow simply by connecting rising point with inflection point or points related to some duration from a hydrograph. Environmental tracer method (ETM) is another tool researched and developed under several conditions to estimate the groundwater recharge. The goal of this study is to separate the baseflow component from a storm hydrograph by applying various GMs and ETM, and to compare their results. The baseflow component estimated by ETM was different from the results by GMs in terms of their shapes of fluctuation and flow rates. Another important feature is that the form of the baseflow to which ETM is applied is similar to that of a storm hydrograph. This similarity is presumed to be due to the selection of tracer that respond quickly to rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.

• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.162-163
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• 2022

Maritime transportation is going to transfer to alternative fuels as a result of the worldwide demands toward decarbonization and tougher maritime emissions regulations. Methanol is considered as a potential marine fuel, which has the ability to reduce SOx and CO₂ emissions, reduce climate change effects, and achieve the objective of green shipping. This work proposes and combines the innovative combination system of direct methanol solid oxide fuel cells (SOFC), proton exchange membrane fuel cells (PEMFC), gas turbines (GT), and organic Rankine cycles (ORC) for maritime vessels. The system's primary power source is the SOFC, while the GT and PEMFC use the waste heat from the SOFC to generate useful power and improve the system's ability to use waste heat. Each component's thermodynamics model and the combined system's model are established and examined. The multigeneration system's energy and exergy efficiency are 76.2% and 30.3%, respectively. When compared to a SOFC stand-alone system, the energy efficiency of the GT and PEMFC system is increased by 19.2%. The use of PEMFC linked SOFC has significant efficiency when a ship is being started or maneuvered and a quick response from the power and propulsion plant is required.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.276-283
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• 2024

Currently, the overall tendency for green and sustainable economic development is creating a circular economy. In actuality, agricultural output is currently benefiting greatly from the growth of the circular economy. The creation of a circular economy helps address resource scarcity, save the environment, combat climate change, and increase economic efficiency. Vietnam's economy can grow quickly and sustainably by shifting to a circular economy production model. Comparing prior growth techniques to the digital age and implementing circular economic development connected with high technology will be a fantastic opportunity to boost growth efficiency. In actuality, Vietnam currently has a large number of agricultural circular economy models. These are models: Creating and using gas from waste and wastewater in livestock and farming; model combining cultivation, livestock, and aquaculture; agro-forestry model; garden-forest model; Circular model using agricultural by-products as a catalyst or creating other valuable products; model of moderation, linked to reducing the use of growth hormones, veterinary medications, pesticides, and artificial fertilizers in agriculture and animal husbandry. Unfortunately, there have been few studies and applications of the aforementioned models, which has made it difficult to build the agricultural sector sustainably. In this paper, we outline the current situation and propose solutions to develop a circular economy model in agriculture in Vietnam for sustainable development.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.73-78
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• 2011

In order to resolve the problem of change in global climate which is worsening as days go by and to preemptively cope with strengthened restriction on carbon emission, the government enacted 'Framework Act on Low Carbon Green Growth' in 2010 and selected green technology and green industry as new national growth engines. For this reason, the necessity to use the un-utilized waste heat across the whole industrial system has become an issue, and studies on and applications of recycling in the agricultural and fishery fields such as cultivation of tropical crops and flatfishes by utilizing the waste heat and thermal effluent generated by large industrial complexes including power plants are being actively carried out. In this study, we looked into the domestic and overseas examples of having utilized waste heat abandoned in the form of power plant thermal effluent, and carried out economic efficiency evaluation of sturgeon aquaculture utilizing thermal effluent of Yeongwol LNG Combined Cycle Power Plant in Gangwon-do. In this analysis, we analyzed the economic efficiency of a model business plan divided into three steps, starting from a small scale in order to minimize the investment risk and financial burden, which is then gradually expanded. The business operation period was assumed to be 10 years (2012~2021), and the NVP (Net Present Value) and economic efficiency (B/C) for the operation period (10 years) were estimated for different loan size by dividing the size of external loan by stage into 80% and 40% based on the basic statistics secured through a site survey. Through the result of analysis, we can see that reducing the size of the external loan is an important factor in securing greater economic efficiency as, while the B/C is 1.79 in the case the external loan is 80% of the total investment, it is presumed to be improved to 1.81 when the loan is 40%. As the findings of this study showed that the economic efficiency of sturgeon aquaculture utilizing thermal effluent of power plant can be secured, it is presumed that regional development project items with high added value can be derived though this, and, in addition, this study will greatly contribute to reinforcement of the capability of local governments to cope with climate change.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.3
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• pp.253-263
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• 1993

Climatological informations have not been fully utilized by agricultural research and extension workers in Korea due mainly to inaccessbilty to the archived climate data. This study was initiated to improve access to historical climate data gathered from 72 weather stations of Korea Meteorological Administration for agricultural applications by using a microcomputer-based methodology. The climatological elements include daily values of average, maximum and minimum temperature, relative humidity, average and maximum wind speed, wind direction, evaporation, precipitation, sunshine duration and cloud amount. The menu-driven, user-friendly data retrieval system(CLIDAS) provides quick summaries of the data values on a daily, weekly and monthly basis and selective retrieval of weather records meeting certain user specified critical conditions. Growing degree days and potential evapotranspiration data are derived from the daily climatic data, too. Data reports can be output to the computer screen, a printer or ASCII data files. CLIDAS can be run on any IBM compatible machines with Video Graphics Array card. To run the system with the whole database, more than 50 Mb hard disk space should be available. The system can be easily upgraded for further expansion of functions due to the module-structured design.

• Journal of Korea Water Resources Association
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• v.47 no.8
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• pp.671-684
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• 2014

Change point analysis is a efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, this fundamental information to change points to future rainfall data identified by reasonable detection skills can affect the prediction of flood and drought occurrence because well detected change points provide a key to resolve the non-stationary or inhomogeneous problem by climate change. Therefore, in this study, the comparative study to assess the performance of the 3 change point detection skills, cumulative sum (CUSUM) method, Bayesian change point (BCP) method, and segmentation by dynamic programming (DP) was performed. After assessment of the performance of the proposed detection skills using the 3 types of the synthetic series, the 2 reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in hydrological modeling considering inhomogeneity or nonstationarity.