• Journal of Environmental Science International
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• v.21 no.4
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• pp.517-533
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• 2012

This study calculated wind speed at the height of 10 m using a disaster prediction model(Florida Public Hurricane Loss Model, FPHLM) that was developed and used in the United States. Using its distributions, a usable information of surface wind was produced for the purpose of disaster prevention when the typhoon attack. The advanced research version of the WRF (Weather Research and Forecasting) was used in this study, and two domains focusing on South Korea were determined through two-way nesting. A horizontal time series and vertical profile analysis were carried out to examine whether the model provided a resonable simulation, and the meteorological factors, including potential temperature, generally showed the similar distribution with observational data. We determined through comparison of observations that data taken at 700 hPa and used as input data to calculate wind speed at the height of 10 m for the actual terrain was suitable for the simulation. Using these results, the wind speed at the height of 10 m for the actual terrain was calculated and its distributions were shown. Thus, a stronger wind occurred in coastal areas compared to inland areas showing that coastal areas are more vulnerable to strong winds.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.713-719
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• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.153-164
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• 2022

In this study, we estimate the quantile-regression framework of the shipping industry for the Capesize used ship, which is a typical raw material transportation from January 2000 to December 2021. This research aims two main contributions. First, we analyze the relationship between the Capesize used ship, which is a typical type in the raw material transportation market, and the freight market, for which mixed empirical analysis results are presented. Second, we present an empirical analysis model that considers the structural transformation proposed in the Hyunsok Kim and Myung-hee Chang(2020a) study in quantile-regression. In structural change investigations, the empirical results confirm that the quantile model is able to overcome the problems caused by non-stationarity in time series analysis. Then, the long-run relationship of the co-integration framework divided into long and short-run effects of exogenous variables, and this is extended to a prediction model subdivided by quantile. The results are the basis for extending the analysis based on the shipping theory to artificial intelligence and machine learning approaches.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.29-39
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• 2017

In this study, the GPM (Global Precipitation Mission) IMERG (Integrated Multi-satellitE retrievals for GPM) rainfall data was verified and evaluated using ground AWS (Automated Weather Station) and radar in order to investigate the availability of GPM IMERG rainfall data. The SPI (Standardized Precipitation Index) was calculated based on the GPM IMERG data and also compared with the results obtained from the ground observation data for the Hoengseong Dam and Yongdam Dam areas. For the radar data, 1.5 km CAPPI rainfall data with a resolution of 10 km and 30 minutes was generated by applying the Z-R relationship ($Z=200R^{1.6}$) and used for accuracy verification. In order to calculate the SPI, PERSIANN_CDR and TRMM 3B42 were used for the period prior to the GPM IMERG data availability range. As a result of latency verification, it was confirmed that the performance is relatively higher than that of the early run mode in the late run mode. The GPM IMERG rainfall data has a high accuracy for 20 mm/h or more rainfall as a result of the comparison with the ground rainfall data. The analysis of the time scale of the SPI based on GPM IMERG and changes in normal annual precipitation adequately showed the effect of short term rainfall cases on local drought relief. In addition, the correlation coefficient and the determination coefficient were 0.83, 0.914, 0.689 and 0.835, respectively, between the SPI based GPM IMERG and the ground observation data. Therefore, it can be used as a predictive factor through the time series prediction model. We confirmed the hydrological utilization and the possibility of real time drought monitoring using SPI based on GPM IMERG rainfall, even though results presented in this study were limited to some rainfall cases.

• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.283-290
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• 2005

Objectives : The aim of this paper was to examine the relationship between the summertime (June to August) heat index, which quantifies the bioclimatic apparent temperature in sultry weather, and the daily disease-related mortality in Seoul for the period from 1991 to 2000. Methods : The daily maximum (or minimum) summertime heat indices, which show synergetic apparent temperatures, were calculated from the six hourly temperatures and real time humidity data for Seoul from 1991 to 2000. The disease-related daily mortality was extracted with respect to types of disease, age and sex, etc. and compared with the time series of the daily heat indices. Results : The summertime mortality in 1994 exceeded the normal by 626 persons. Specifically, blood circulation-related and cancer-related mortalities increased in 1994 by 29.7% (224 persons) and 15.4% (107 persons), respectively, compared with those in 1993. Elderly persons, those above 65 years, were shown to be highly susceptible to strong heat waves, whereas the other age and sex-based groups showed no significant difference in mortality. In particular, a heat wave episode on the 22nd of July 2004 ($>45^{\circ}C$ daily heat index) resulted in double the normal number of mortalities after a lag time of 3 days. Specifically, blood circulation-related mortalities, such as cerebral infraction, were predominant causes. Overall, a critical mortality threshold was reached when the heat index exceeded approximately $37^{\circ}C$, which corresponds to human body temperature. A linear regression model based on the heat indices above $37^{\circ}C$, with a 3 day lag time, accounted for 63% of the abnormally increased mortality (${\geq}+2$ standard deviations). Conclusions : This study revealed that elderly persons, those over 65 years old, are more vulnerable to mortality due to abnormal heat waves in Seoul, Korea. When the daily maximum heat index exceeds approximately $37^{\circ}C$, blood circulation-related mortality significantly increases. A linear regression model, with respect to lag-time, showed that the heat index based on a human model is a more dependable indicator for the prediction of hot weather-related mortality than the ambient air temperature.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.29 no.2
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• pp.143-149
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• 1996

Forecasts of the monthly catches of anchovy in Korea were carried out by the seasonal Autoregressive Integrated Moving Average (ARIMA) model and spectral analysis. The seasonal ARIMA model is as follows: $$(1-0.431B)(1-B^{12})Z_t=(1-0.882B^{12})e_t$$ where: $Z_t=value$ at month $t;\;B^{p}$ is a backward shift operator, that is, $B^pZ_t=Z_{t-p};$ and $e_t=error$ term at month t, which is to forecast 24 months ahead the anchovy catches in Korea. The prediction error by the Box-Cox transformation on monthly anchovy catches in Korea was less than that by the logarithmic transformation. The equation of the Box-Cox transformation was $Y'=(Y^{0.58}-1)/0.58$. Forecasts of the monthly anchovy catches for $1991\~1992$, which were compared with the actual catches, had an absolute percentage error (APE) range of $1.0\~63.2\%$. Total observed annual catches in 1991 and 1992 were 170,293 M/T and 168,234 M/T respectively, while the predicted catches were 148,201 M/T and 148,834 M/T $(API\;13.0\%\;and\;11.5\%,\;respectively)$. The spectrum analysis of the monthly catches of anchovy showed some dominant fluctuations in the periods of 2.2, 6.1, 10.2 12.0 and 14.7 months. The spectrum analysis was also useful for selecting the ARIMA model.

• Journal of Korea Water Resources Association
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• v.45 no.5
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• pp.481-493
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• 2012

The Uncertainty of drought events can be regarded as supernatural phenomena so that the uncertainty of water supply system will be also uncontrollable. Decision making for water supply system operation must be dealt with in consideration of hydrologic uncertainty conditions. When ultimate small quantity of precipitation or streamflow lasts, water supply system might be impacted as well as stream pollution, aqua- ecosystem degradation, reservoir dry-up and river aesthetic waste etc. In case of being incapable of supplying water owing to continuation of severe drought, it can make the damage very serious beyond our prediction. This study analyzes comprehensively sustained drought impacts on the Han River Basin Water Supply System. Drought scenarios consisted of several sustained times and return periods for 5 sub-watersheds are generated using a stochastic hydrologic time series model. The developed drought scenarios are applied to assess water supply performance at the Paldang Dam. The results show that multi-year drought events reflecting spatial hydrologic diversity need to be examined in order to recognize variation of the unexpected drought impacts.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.3
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• pp.375-388
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• 2019

Accurate evaluation of sea-to-air $CO_2$ flux and its variability is crucial information to the understanding of global carbon cycle and the prediction of atmospheric $CO_2$ concentration. $fCO_2$ observations are sparse in space and time in the East Sea. In this study, we derived high resolution time series of surface $fCO_2$ values in the southwest East Sea, by feeding sea surface temperature (SST), salinity (SSS), chlorophyll-a (CHL), and mixed layer depth (MLD) values, from either satellite-observations or numerical model outputs, to three machine learning models. The root mean square error of the best performing model, a Random Forest (RF) model, was $7.1{\mu}atm$. Important parameters in predicting $fCO_2$ in the RF model were SST and SSS along with time information; CHL and MLD were much less important than the other parameters. The net $CO_2$ flux in the southwest East Sea, calculated from the $fCO_2$ predicted by the RF model, was $-0.76{\pm}1.15mol\;m^{-2}yr^{-1}$, close to the lower bound of the previous estimates in the range of $-0.66{\sim}-2.47mol\;m^{-2}yr^{-1}$. The time series of $fCO_2$ predicted by the RF model showed a significant variation even in a short time interval of a week. For accurate evaluation of the $CO_2$ flux in the Ulleung Basin, it is necessary to conduct high resolution in situ observations in spring when $fCO_2$ changes rapidly.