• Korean System Dynamics Review
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• v.15 no.3
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• pp.5-36
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• 2014

The purpose of this paper is to develop a System Dynamics model for estimating the volume of forest resources in the future and simulating the volume of function of public benefit linked to forest resources in dynamic manner. Also it is to analyze the impact when the volume of forest land conversion is controlled by policy using the SD model. The analysis was done at nation-wide for the simulation period 2000 to 2040. Estimated forest area was 6.2 million ha and estimated growing stock was $4.7\;billion\;m^3$ in 2040 from the future forecast without policies. Changing of forest resources, 13.9 billion tons of forest-ground-water storage was estimated, $1.8\;million\;m^3$ of erosion control of forest was estimated and 377 million tons of $CO_2$ absorption was estimated. As a result of simulation with two alternatives, forest area was less reduced and growing stock was bigger than do nothing policy. Also, function of public benefit reflected by changes of forest resources was enhanced. This study contributes to estimate the quantitatively measured volume of forest resources and function of public benefit over the 30 years in Korean forest land in scientific way. Using this SD model, decision maker would develop forest land policies more delicately for deserving forest resources and increasing the volume of function of public.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.125-135
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• 2020

The uncontrolled urban expansion causes various social, economic problems and natural/environmental problems. Therefore, it is necessary to forecast urban expansion by identifying various factors related to urban expansion. This study aims to forecast it using a decision tree that is widely used in various areas. The study used geographic data such as the area of use, geographical data like elevation and slope, the environmental conservation value assessment map, and population density data for 2006 and 2018. It extracted the new urban expansion areas by comparing the residential, industrial, and commercial zones of the zoning in 2006 and 2018 and derived a decision tree using the 2006 data as independent variables. It is intended to forecast urban expansion in 2030 by applying the data for 2018 to the derived decision tree. The analysis result confirmed that the distance from the green area, the elevation, the grade of the environmental conservation value assessment map, and the distance from the industrial area were important factors in forecasting the urban area expansion. The AUC of 0.95051 showed excellent explanatory power in the ROC analysis performed to verify the accuracy. However, the forecast of the urban area expansion for 2018 using the decision tree was 15,459.98㎢, which was significantly different from the actual urban area of 4,144.93㎢ for 2018. Since many regions use decision tree to forecast urban expansion, they can be useful for identifying which factors affect urban expansion, although they are not suitable for forecasting the expansion of urban region in detail. Identifying such important factors for urban expansion is expected to provide information that can be used in future land, urban, and environmental planning.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.612-620
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• 2013

In this study, time series analysis was tried, which is widely applied to demand forecast of diverse fields such as finance, economy, trade, and so on, different from previous regression analysis. Future marine traffic volume was forecasted on the basis of data of the number of ships entering Incheon port from January 1996 to June 2013, through courses of stationarity verification, model identification, coefficient estimation, and diagnostic checking. As a result of prediction January 2014 to December 2015, February has less traffic volume than other months, but January has more traffic volume than other months. Also, it was found out that Incheon port was more proper to ARIMA model than exponential smoothing method and there was a difference of monthly traffic volume according to seasons. The study has a meaning in that future traffic volume was forecasted per month with time series model. Also, it is judged that forecast of future marine traffic volume through time series model will be the more suitable model than prediction of marine traffic volume with previous regression analysis.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1249-1263
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• 2013

In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.363-372
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• 2017

This study analyzed how management earnings forecasts would have an effect on future earnings quality. The analysis period of study was from 2003 till 2009 (ofrom 2004 till 2011) based on variables of interest (dependent variables) and the annual data from a total of 475 companies that publicly announced manager's operating earnings forecasts among securities listing companies were used for analysis. As a result, first, it appeared that the more optimistic the manager's earnings forecasts were for the current term, the lower the future earnings quality was. Second, it was found that the lower the accuracy of the manager's earnings forecasts was for the current term, the lower the future earnings quality was. Such findings suggest that management earnings forecasts will be used for determining future earnings quality.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.769-779
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• 2017

As the occurrence of drought is recently on the rise, the reliable drought forecasting is required for developing the drought mitigation and proactive management of water resources. This study developed a probabilistic hydrological drought forecasting method using the Bayesian Networks and drought propagation relationship to estimate future drought with the forecast uncertainty, named as the Propagated Bayesian Networks Drought Forecasting (PBNDF) model. The proposed PBNDF model was composed with 4 nodes of past, current, multi-model ensemble (MME) forecasted information and the drought propagation relationship. Using Palmer Hydrological Drought Index (PHDI), the PBNDF model was applied to forecast the hydrological drought condition at 10 gauging stations in Nakdong River basin. The receiver operating characteristics (ROC) curve analysis was applied to measure the forecast skill of the forecast mean values. The root mean squared error (RMSE) and skill score (SS) were employed to compare the forecast performance with previously developed forecast models (persistence forecast, Bayesian network drought forecast). We found that the forecast skill of PBNDF model showed better performance with low RMSE and high SS of 0.1~0.15. The overall results mean the PBNDF model had good potential in probabilistic drought forecasting.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.165-170
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• 2022

The amount of data generated by recent developments in Big data and related technologies has been rapidly increasing, and the need to predict changes in future societies and present technologies to be realized has been continuously raised to lay the foundation for national scientific and technological planning. The existing methods of predicting future technologies have their respective advantages, but problems also exist. Thus, this paper newly establishes and applies the methodology to be used for predicting future technologies specialized in information security fields beyond the existing comprehensive prediction, and draws out innovative technologies that are expected to have high ripple effects in the future, and analyzes the technological diffusion points of each technology to predict future technological changes in the information security sector. It is expected that this will ensure reliability and objectivity of the forecast survey results and allow more sophisticated and multilayered predictions than the overall scientific and technological forecast surveys.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.182-189
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• 2017

Photovoltaic power generation systems make up a large part of the low carbon energy trend. The purpose of this study is to utilize PVsyst, a commercial forecasting program, to forecast research on the design stages of photovoltaic power generation for wider applications of this system in railroads and to consider prospective issues for photovoltaic power plants that are currently being operated. Given this, we will compare the forecast value of generated photovoltaic power, derived from foreign weather forecast information provided by NASA, along with information from Meteonorm, and the forecast values derived from the KMA weather information. By comparing these values with amounts actually generated by KPX, this research aims to secure propriety rights for wider application of photovoltaic power generation systems in railroads, and to contribute to low carbon energy for the new climate of the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.197-197
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• 2016

Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.