• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.723-734
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• 2021

Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.

• Particle and aerosol research
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• v.15 no.3
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• pp.91-104
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• 2019

In this study, hourly measurements of $PM_{2.5}$ and its major chemical constituents such as organic and elemental carbon (OC and EC), and ionic species were made between January 15 and February 10, 2018 at the air pollution intensive monitering station in Gwangju. In addition, 24-hr integrated $PM_{2.5}$ samples were collected at the same site and analyzed for OC, EC, water-soluble OC (WSOC), humic-like substance (HULIS), and ionic species. Over the whole study period, the organic aerosols (=$1.6{\times}OC$) and $NO_3{^-}$ concentrations contributed 26.6% and 21.0% to $PM_{2.5}$, respectively. OC and EC concentrations were mainly attributed to traffic emissions with some contribution from biomass burning emissions. Moreover, strong correlations of OC with WSOC, HULIS, and $NO_3{^-}$ suggest that some of the organic aerosols were likely formed through atmospheric oxidation processes of hydrocarbon compounds from traffic emissions. For the period between January 18 and 22 when $PM_{2.5}$ pollution episode occurred, concentrations of three secondary ionic species ($=SO{_4}^{2-}+NO_3{^-}+NH_4{^+}$) and organic matter contributed on average 50.8 and 20.1% of $PM_{2.5}$, respectively, with the highest contribution from $NO_3{^-}$. Synoptic charts, air mass backward trajectories, and local meteorological conditions supported that high $PM_{2.5}$ pollution was resulted from long-range transport of haze particles lingering over northeastern China, accumulation of local emissions, and local production of secondary aerosols. During the $PM_{2.5}$ pollution episode, enhanced $SO{_4}^{2-}$ was more due to the long-range transport of aerosol particles from China rather than local secondary production from $SO_2$. Increasing rate in $NO_3{^-}$ was substantially greater than $NO_2$ and $SO{_4}^{2-}$ increasing rates, suggesting that the increased concentration of $NO_3{^-}$ during the pollution episode was attributed to enhanced formation of local $NO_3{^-}$ through heterogenous reactions of $NO_2$, rather than impact by long-range transportation from China.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.299-311
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• 2020

This paper analyzes the distribution of water vapor pressure and relative humidity in complex terrains by collecting weather observation data at 6 locations in the valley in Jungdae-ri, Ganjeon-myeon, Gurye-gun, Jeolla South Province and 14 locations in Akyang-myeon, Hadong-gun, Gyeongsang South Province, which form a single drainage basin in rural and mountainous regions. Previously estimated water vapor pressure used in the early warning system for agrometeorological hazard and actual water vapor pressure arrived at using the temperature and humidity that were measured at the highest density (1.5 m above ground) at every hour in the valley of Jungdae-ri between 19 December 2014 and 23 November 2015 and in the valley of Akyang between 15 August 2012 and 18 August 2013 were compared. The altitude-specific gradient of the observed water vapor pressure varied with different hours of the day and the difference in water vapor pressure between high and low altitudes increased in the night. The hourly variations in the water vapor pressure in the weather stations of the valley of Akyang with various topographic and ground conditions were caused by factors other than altitude. From the observed data of the study area, a coefficient that adj usts the variation in the water vapor pressure according to the specific difference in altitude and estimates it closer to the actual measured level was derived. Relative humidity was simulated as water vapor pressure estimated against the saturated water vapor pressure, thus, confirming that errors were further reduced using the derived coefficient than with the previous method that was used in the early warning system.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.87-96
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• 2021

Determining design rainfall is the first step to plan an agricultural drainage facility. The objective of this study is to evaluate whether the current method for parameter estimation is reasonable for computing the design rainfall. The current Gumbel-Kendall (G-K) method was compared with two other methods which are Gumbel-Chow (G-C) method and Probability weighted moment (PWM). Hourly rainfall data were acquired from the 60 ASOS (Automated Synoptic Observing System) stations across the nation. For the goodness-of-fit test, this study used chi-squared (𝛘²) and Kolmogorov-Smirnov (K-S) test. When using G-K method, 𝛘² statistics of 18 stations exceeded the critical value (𝑥²_a=0.05,df=4=9.4877) and 10, 3 stations for G-C method, PWM method respectively. For K-S test, none of the stations exceeded the critical value (D^a=0.05_n=0.19838). However, G-K method showed the worst performances in both tests compared to other methods. Subsequently, this study computed design rainfall of 48-hour duration in 60 ASOS stations. G-K method showed 5.6 and 6.4% higher average design rainfall and 15.2 and 24.6% higher variance compared to G-C and PWM methods. In short, G-K showed the worst performance in goodness-of-fit tests and showed higher design rainfall with the least robustness. Likewise, considering the basic assumptions of the design rainfall estimation, G-K is not an appropriate method for the practical use. This study can be referenced and helpful when revising the agricultural drainage standards.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1339-1348
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• 2020

Phytoplankton controls marine ecosystems in terms of nutrients, photosynthetic rate, carbon cycle, etc. and the degree of its influence on the marine environment depends on their physical size. Many studies have been attempted to identify marine phytoplankton size classes using the remote sensing techniques. One of successful approach was the three-component model which estimates the chlorophyll concentrations of three phytoplankton size classes (micro-phytoplankton; >20 ㎛, nano-; 2-20 ㎛ and pico-; <2 ㎛) as a function of total chlorophyll. Here, we examined the applicability of Geostationary Ocean Colour Imager (GOCI) to the mapping of the phytoplankton size class distribution in the East Sea. A fit of the three-component model to a biomarker pigment dataset collected in the study area for some years including a large harmful algal bloom period has been carried out to derive size-fractioned chlorophyll concentration (CHL). The tuned three-component model was applied to the hourly GOCI images to identify the fractions of each phytoplankton size class for the entire CHL. Then, we investigated the distribution of phytoplankton community in terms of the size structure in the East Sea during the harmful Cochlodinium polykrikoides blooms in the summer of 2013.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Conservation Science in Museum
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• v.28
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• pp.51-64
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• 2022

This study aims to identify the impact of optical energy on cultural properties when the light energy irradiates cultural assets during augmented reality (AR) or media façade performances as activities designed to garner public interest. The 10-story Gyeongcheonsa Pagoda was used for this study, and the impact was evaluated by comparing the optical energy irradiated during a media façade performance with the energy irradiated under normal conditions. For comparison, this study measured the illuminance in lux for each light source that irradiated the ten-story stone pagoda and used the data to calculate illuminance in lux-hours. The results showed that the pagoda receives 786.4 lux per hour when both sunlight and artificial light are present, while 13.2 lux of energy is irradicated by the media façade for each performance. The result indicates that the pagoda receives about 29.8 times more optical energy from sunlight and artificial light sources than during media façade performances on an hourly basis, when the performance is carried out twice a week. This study therefore concludes that the optical energy of media façade performances inflicted trivial damage to the ten-story stone pagoda.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.913-922
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• 2022

The purpose of this research is to propose the measurement of improving DEM by using the water surface range of SAR image analysis for river corridors and to suggest the construction of satellite-based 3D river spatial information of inaccessible regions such as North Korea. For this research, it has been progressed from the accessible area, watershed of Namgang river, the branch of Nakdonggang river. The satellite image was collected from SAR satellite image data for a year in 2021 which was provided by ESA from Sentinel-1A/B data and extracted from the seasonal water surface area. Ground gauge water level was collected from hourly intervals data by WAMIS. The DEM was improved by analysis of the river altitude of water surface area change by the combination of the ground water level of minimum to maximum water surface area data extracted from SAR image analysis. After the improvement of DEM, the altitude of the river varied that it is defined to comprise more natural form of river DEM than the existing DEM. The correction validation of improvement DEM was necessary in field survey elevation data; however, the correction validation was not progressed due to the absence of the data. Although, the purpose of this research is to provide the improvement of DEM by using the analyzed water surface by existing DEM data and SAR image analysis. After the progression of additional correction validation research, we would plan to examine the application in other places and to progress the additional methodological research to apply in inaccessible and unmeasured area including the North Korea.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.205-232
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• 2022

This study analyzes the effect of the 2^nd pilot test of Tiime of Use (TOU) pricing for Korean households using a two-level electricity demand model. The test, implemented from May to September 2021, was conducted to compare the effects of two TOU pricing rates and the standard rates for households living in apartment and detached house in 7 provinces of Korea. Based on the data on electricity consumption during the test period and during the same period last year of the 1,292 participants and their socio-economic characteristics, this study analyzes (1) whether the relative demand across periods has changed in response to hourly price changes and (2) whether the price responsiveness of daily consumption has changed after the introduction of TOU pricing. The results show that both types of TOU pricing affect neither the relative demand across periods nor the price responsiveness of daily consumption. The reason behind the results could be related to the level of TOU pricing rates and the periodical classification, which were not sufficient to induce changes in the participants' electricity demand patterns.