• 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 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 Korean Society for Aviation and Aeronautics
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• v.5 no.1
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• pp.51-63
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• 1997

Generally, air freight forecasting model used to major factor GNP(GDP), Yield, Exchange rate, as its independent variables. We studied about the factors that affect to Air Freight in Korea, and we found six affective variables. Those are GNP, Exchange rate, Flight routes, Flight numbers, Sum of dollars Export and import. To find the relationship between the Air Freight and GNP, Exchange rate, Flight routes, Flight numbers, Sum of dollars Export and import we used regression analysis. Through the regression analysis, we found some problems in the model. There are collieneraities between the variables, so we took the variables selection model to choose the best affective variables of air cargo. We have defined the the Korean air freight forecasting model with two variables and forecast far the $1996{\sim}2010$ period were made by using this model.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.153-153
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• 2017

• Atmosphere
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• v.25 no.1
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• pp.67-83
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• 2015

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.257-267
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• 2020

In this study, we predicted the employment ratio by the export industry using various machine learning methods and verified whether the prediction performance is improved by applying the constrained forecast combination method to these predicted values. In particular, the constrained forecast combination method is known to improve the prediction accuracy and stability by imposing the sum of predicted values' weights up to one. In addition, this study considered various variables affecting the employment ratio of each industry, and so we adopted recursive feature elimination method that allows efficient use of machine learning methods. As a result, the constrained forecast combination showed more accurate prediction performance than the predicted values of the machine learning methods, and in particular, the stability of the prediction performance of the constrained forecast combination was higher than that of other machine learning methods.

• Atmosphere
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• v.21 no.1
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• pp.85-93
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• 2011

UK Met Office Unified Model (UM) is a grid model applicable for both global and regional model configurations. The Met Office has developed a 4D-Var data assimilation system, which was implemented in the global forecast system on 5 October 2004. In an effort to improve its Numerical Weather Prediction (NWP) system, Korea Meteorological Administration (KMA) has adopted the UM system since 2008. The aim of this study is to provide the basic information on the effects of satellite data assimilation on UM performance by conducting global satellite data denial experiments. Advanced Tiros Operational Vertical Sounder (ATOVS), Infrared Atmospheric Sounding Interferometer (IASI), Special Sensor Microwave Imager Sounder (SSMIS) data, Global Positioning System Radio Occultation (GPSRO) data, Air Craft (CRAFT) data, Atmospheric Infrared Sounder (AIRS) data were assimilated in the UM global system. The contributions of assimilation of each kind of satellite data to improvements in UM performance were evaluated using analysis data of basic variables; geopotential height at 500 hPa, wind speed and temperature at 850 hPa and mean sea level pressure. The statistical verification using Root Mean Square Error (RMSE) showed that most of the satellite data have positive impacts on UM global analysis and forecasts.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• The Plant Pathology Journal
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• v.35 no.6
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• pp.585-597
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• 2019

A disease forecast model for Marssonina blotch of apple was developed based on field observations on airborne spore catches, weather conditions, and disease incidence in 2013 and 2015. The model consisted of the airborne spore model (ASM) and the daily infection rate model (IRM). It was found that more than 80% of airborne spore catches for the experiment period was made during the spore liberation period (SLP), which is the period of days of a rain event plus the following 2 days. Of 13 rain-related weather variables, number of rainy days with rainfall ≥ 0.5 mm per day (L_day), maximum hourly rainfall (P_max) and average daily maximum wind speed (W_avg) during a rain event were most appropriate in describing variations in airborne spore catches during SLP (S_i) in 2013. The ASM, Ŝ_i = 30.280+5.860×L_day×P_max-2.123×L_day×P_max×W_avg was statistically significant and capable of predicting the amount of airborne spore catches during SLP in 2015. Assuming that airborne conidia liberated during SLP cause leaf infections resulting in symptom appearance after 21 days of incubation period, there was highly significant correlation between the estimated amount of airborne spore catches (Ŝ_i) and the daily infection rate (R_i). The IRM, ${\hat{R}}_i$ = 0.039+0.041×Ŝ_i, was statistically significant but was not able to predict the daily infection rate in 2015. No weather variables showed statistical significance in explaining variations of the daily infection rate in 2013.

• Journal of Korea Port Economic Association
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• v.20 no.2
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• pp.151-167
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• 2004

Traditional traffic forecast has employed regression analysis or time-series analysis based on past trends of explanatory variables. However, not existing but planned port facilities do not have historical data for traffic estimation. Consequently, arbitrary traffic allocation has been subject to researcher's intuition. In this paper, container throughput at New Incheon Outer-South Port will be estimated using stated preference(SP) and sample enumeration methodology on the basis of survey data about the choice behaviors of port users in a theoretical situation. In the SP survey, shippers, freight forwarders and carriers were required to answer a choice between two alternative ports: Busan and Incheon. Although total 27 scenarios of questionnaires were constructed with 3 levels of 3 explanatory variables, each interviewee was asked to answer for just 9 scenarios chosen at random. A binary choice logit model was applied to the survey data. The elasticity of travel time is estimated to be very high, implying that building New Incheon Outer-South Port could be effective in relieving the congestion of the Kyungin corridor. The analysis result shows that increasing service level at Incheon Port would bring in the substantial diversion of container cargo in the Capital region to Incheon Port from Busan Port.