• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.281-289
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• 2015

Information on sunshine duration and solar radiation are indispensable to the understanding of crop growth and development. Yet, relevant variables are not available in the Korea Meteorological Administration's (KMA) digital forecast. We proposed the methods of estimating sunshine duration and solar radiation based on the 'sky condition' of digital forecast products and validated using the observed data. The sky condition values (1 for clear, 2 for partly cloudy, 3 for cloudy, and 4 for overcast) were collected from 22 weather stations at 3-hourly intervals from August 2013 to July 2015. According to the observed relationship, these data were converted to the corresponding amount of clouds on the 0 to 10 scale (0 for clear, 4 for partly cloudy, 7 for cloudy, and 10 for overcast). An equation for the cloud amount-sunshine duration conversion was derived from the 3-year observation data at three weather stations with the highest clear day sunshine ratio (i.e., Daegwallyeong, Bukgangneung, and Busan). Then, the estimated sunshine hour data were used to run the Angstrom-Prescott model which was parameterized with the long-term KMA observations, resulting in daily solar radiation for the three weather stations. Comparison of the estimated sunshine duration and solar radiation with the observed at those three stations showed that the root mean square error ranged from 1.5 to 1.7 hours for sunshine duration and from 2.5 to $3.0MJ\;m^{-2}\;day^{-1}$ for solar radiation, respectively.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Korean System Dynamics Review
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• v.9 no.2
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• pp.105-128
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• 2008

City of Gyeongju's referendum finally offered the long-waited low-level radioactive waste disposal site in November 2005. Gyeongju's positive decision was due to the various economic rewards and incentives the national government promised to the city. 300 billion won for an accepting bonus, the location of the headquarter building of the Korean Hydro and Nuclear Power Co., and the accelerator research center and 3.25 trillion won for supporting regional development program implementation. All of the above will affect the city's infrastructure and the citizens' economic and social lives. Population, land use, economic structure, SOC and quality of life will be affected. Some will be very positive, and some will be negative. This research project will see the future of the city and forecast the demographic, economic, physical and environmental changes of the city via computer simulation's system dynamics technique. This kind of simulation will help City of Gyeongju's what to prepare for the future. The population forecasting of the year 2046 will be 662,424 with the waste disposal site, and 327,274 without the waste disposal site in Gyeongju. The waste disposal site and regional supporting program will increase 184,246 Jobs more with 1,605 agriculture and fishery, 5,369 manufacturing shops and 27,577 shops. The population increase will bring 96,726 more houses constructed in the city. Land use will also be affected. More land will be developed. And road, water plant and waste water plant will be expanded as much. The city's financial structure will be expanded, due to the increased revenues from the waste disposal site, and property tax revenues from the middle-class employees of the company, and the high-powered scientists and technologists from the accelerator research center. All in all, the future of the city will be brighter after operating the nuclear waste disposal site inside the city.

• Journal of Ecology and Environment
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• v.38 no.1
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• pp.119-122
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• 2015

It is very useful and important to see the status and change of necessary parts in a short period through selecting and observing the bioindicator continually to forecast and prepare the future. Especially, living things are so closely related to the environment that the indicator between the environment and living things shows close interrelationship. Also, the indicator related to environment provides information about representative or decisive environmental phenomenon and is used to simplify complicated facts. Considering wide range of background and application including various indicators such as the change-, destruction-, pollution-, and restoration of habitats, climate change, and species diversity, the closest category includes "environmental indicator," "ecological indicator," and "biodiversity indicator." The selection and use of bioindicator is complicated and difficult. The necessary conditions for the indicator selection are flexible and greatly depend on the goals of investigation such as the indicator for biological diversity investigation of specific area, the indicator for habitat destruction, the indicator for climate change, and the indicator for polluted area. It should meet many various conditions to select a good indicator. In this study, eleven selection standards are established based on domestic and overseas studies on bioindicator selection: species with clear classification and ecology, species distributed in geographically widespread area, species that show clear habitat characteristics, species that can provide early warning for a change, species that are easy and economically benefited for the investigation, species that have many independent individual groups and that is not greatly affected by the size of individual groups, species that is thought to represent the response of other species, species that represent the ecology change caused by the pressure of human influence, species for which researches on climate change have been done, species that is easy to observe, appears for a long time and forms a group with many individuals, and species that are important socially, economically, and culturally.

• Journal of the Korean GEO-environmental Society
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• v.21 no.5
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• pp.5-11
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• 2020

Outliers detection and elimination included in field monitoring datum are essential for effective foundation of unusual movement, long and short range forecast of stability and future behavior to various structures. Integrated outlier analysis system for assessing long term time series data was developed in this study. Outlier analysis could be conducted in two step of primary analysis targeted at single dataset and second multi datasets analysis using synthesis value. Integrated outlier analysis system presents basic information for evaluating stability and predicting movement of structure combined with real-time safety management platform. Field application results showed increased correlation between synthesis value including similar sort of sensor showing constant trend and each single dataset. Various monitoring data in case of showing different trend can be used to analyse outlier through correlation-weighted value.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.187-192
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• 2023

The United States (US) National Science Foundation's (NSF's) National Ecological Observatory Network (NEON) is a continental-scale observation facility, constructed and operated by Battelle, that collects long-term ecological data to better understand and forecast how US ecosystems are changing. All data and samples are collected using standardized methods at 81 field sites across the US and are freely and openly available through the NEON data portal, application programming interface (API), and the NEON Biorepository. NSF led a decade-long design process with the research community, including numerous workshops to inform the key features of NEON, culminating in a formal final design review with an expert panel in 2009. The NEON construction phase began in 2012 and was completed in May 2019, when the observatory began the full operations phase. Full operations are defined as all 81 NEON sites completely built and fully operational, with data being collected using instrumented and observational methods. The intent of the NSF is for NEON operations to continue over a 30-year period. Each challenge encountered, problem solved, and risk realized on NEON offers up lessons learned for constructing and operating distributed ecological data collection infrastructure and data networks. NEON's construction phase included offices, labs, towers, aquatic instrumentation, terrestrial sampling plots, permits, development and testing of the instrumentation and associated cyberinfrastructure, and the development of community-supported collection plans. Although colocation of some sites with existing research sites and use of mostly "off the shelf" instrumentation was part of the design, successful completion of the construction phase required the development of new technologies and software for collecting and processing the hundreds of samples and 5.6 billion data records a day produced across NEON. Continued operation of NEON involves reexamining the decisions made in the past and using the input of the scientific community to evolve, upgrade, and improve data collection and resiliency at the field sites. Successes to date include improvements in flexibility and resilience for aquatic infrastructure designs, improved engagement with the scientific community that uses NEON data, and enhanced methods to deal with obsolescence of the instrumentation and infrastructure across the observatory.

• Korean Journal of Agricultural Science
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• v.10 no.2
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• pp.393-405
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• 1983

The demand for livestock and poultry products including beef, pork, chicken, egg and milk whose income elasticities are relatively higher than other staple foods, has been increased significantly during the past two decades in response to the remarkable increase in per capita GNP. This trend will be continued during the fifth and the sixth five year economic development plan period beginning with 1982. The annual GNP growth rate will be 7.5% on the average during the next 10 years. It is greatly needed to estimate the demand for beef, pork, chicken egg and milk and to study the feasibilities of domestic production of livestock products for the formulation of adequate policies in order to equate the consumption and the production during the 1980s. So this study reviewed the possible changes in the food consumption patterns during the 1980s, estimated the demand for beef, pork, chicken, egg and milk by using empirical demand functions and finally made suggestions for the formulation of long term price stabilization policies for each livestock, poultry and dairy products through the equilibrium of the quantity of demand for and supply of the products. There are many factors affecting the demand for meats, but this study considered own price, prices of supplements and substitutes and per capita income as the independent variables in the demand equations. It was found that it's own price and income were most significantly affecting factors among others and the degree of substitution effects were remarkably different among the products. According to the meat demand derived in this study, per capita consumption of beef, pork and chicken in the base year 1982 was 11.2kg for total meat, 2.5kg beef, 6.0kg pork and 2.5kg chicken, 106 pieces egg, 15.1kg milk respectively, while those in 1991 were 19.3kg for total meat, 4.8kg beef, 9.6kg pork, 4.9kg chicken, 133pieces egg and 44.1kg milk. It is also predicted through this study that, when the level of production costs be maintained, the domestic production of pork and chicken will meet the demand for them during the fifth and sixth five year economic plan period. However, there will be chronic shortage of beef supply during the coming years. The annual import requirement will be 30,000tons to 40,000tons during the period. In order to stabilize the domestic livestock and poultry and dairy products market, the government should introduce measures to curb the increase in beef consumption by encouraging the consumption of pork and chicken. For this, the livestock production policy measures should be concentrated on : 1) the improvement of infrastructures of beef production by introducing advanced feeding and management technology, subsidies for the establishment of facilities and price support programs for farmers : 2) the development of dairy beef : 3) the reinforcement of the forecast systems for pork and chicken production and consumption.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.