• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.65-73
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• 2001

Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.365-382
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• 2016

The results from the Global Positioning System (GPS) measurements of the Mobile Observation Vehicle (MOVE) on the eastern coast of Korea have been compared with REFerence (REF) values from the fixed GPS sites to assess the performance of Precipitable Water Vapor (PWV) retrievals in a kinematic environment. MOVE-PWV retrievals had comparatively similar trends and fairly good agreements with REF-PWV with a Root-Mean-Square Error (RMSE) of 7.4 mm and $R^2$ of 0.61, indicating statistical significance with a p-value of 0.01. PWV retrievals from the June cases showed better agreement than those of the other month cases, with a mean bias of 2.1 mm and RMSE of 3.8 mm. We further investigated the relationships of the determinant factors of GPS signals with the PWV retrievals for detailed error analysis. As a result, both MultiPath (MP) errors of L1 and L2 pseudo-range had the best indices for the June cases, 0.75-0.99 m. We also found that both Position Dilution Of Precision (PDOP) and Signal to Noise Ratio (SNR) values in the June cases were better than those in other cases. That is, the analytical results of the key factors such as MP errors, PDOP, and SNR that can affect GPS signals should be considered for obtaining more stable performance. The data of MOVE can be used to provide water vapor information with high spatial and temporal resolutions in the case of dramatic changes of severe weather such as those frequently occurring in the Korean Peninsula.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.189-204
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• 2018

This study has developed a forest fire occurrence probability model for inaccessible areas such as North Korea and Demilitarized Zone and we have developed a real-time forest fire danger rating system that can be used in fire-related works. There are limitations on the research that it is impossible to conduct site investigation for data acquisition and verification for forest fire weather index model and system development. To solve this problem, we estimated the fire spots in the areas where access is impossible by using MODIS satellite data with scientific basis. Using the past meteorological reanalysis data(5㎞ resolution) produced by the Korea Meteorological Administration(KMA) on the extracted fires, the meteorological characteristics of the fires were extracted and made database. The meteorological factors extracted from the forest fire ignition points in the inaccessible areas are statistically correlated with the forest fire occurrence and the weather factors and the logistic regression model that can estimate the forest fires occurrence(fires 1 and non-fores 0). And used to calculate the forest fire weather index(FWI). The results of the statistical analysis show that the logistic models(p<0.01) strongly depends on maximum temperature, minimum relative humidity, effective humidity and average wind speed. The logistic regression model constructed in this study showed a relatively high accuracy of 66%. These findings may be beneficial to the policy makers in Republic of Korea(ROK) and Democratic People's Republic of Korea(DPRK) for the prevention of forest fires.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.3
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• pp.15-23
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• 2014

There have been CFIT(Controlled Flight Into Terrain) accidents that can be prevented if the crew executed go-around. This study is to analyse the common factors of three typical CFIT accidents of Korea in TEM(threat and error management) frame, and the examples of go-around gate and the countermeasures of eight airlines through the survey facilitating go-around to prevent CFIT. The common factors found in three typical CFIT accidents occurred in Korea or by Korean carriers turned out to be in mountainous terrain, in bad weather while in non-precision approach or circling approach by captain as PF(Pilot Flying) when crew make monitoring errors and communication errors. It also turned out that the crew in all three typical tragic CFIT accidents did not execute go-around in unstabilized approaches. The captains did not respond immediately when first officers advised them to go-around until it is too late. Seven out of eight Airlines answered that they use stabilized approach height as 1,000 feet to be stabilized earlier to have more safety margin by enhancing go-around gate regardless of the weather to prevent CFIT in the survey.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.12-28
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• 2019

Road geometry is one of the many factors that cause crashes, but the effect on traffic accident depends on weather conditions even under the same road geometry. This study identifies the variables affecting the crash severity by matching the highway accident data and weather data for 14 years from 2001 to 2014. A hierarchical ordered Logit model is used to reflect the effects of road geometry and weather condition interactions on crash severity, as well as the correlation between individual crashes in a region. Among the hierarchical models, we apply a random intercept model including interaction variables between road geometry and weather condition and a random coefficient model including regional weather characteristics as upper-level variables. As a result, it is confirmed that the effects of toll, ramp, downhill slope of 3% or more, and concrete barrier on the crash severity vary depending on weather conditions. It also shows that the combined effects of road geometry and weather conditions may not be linear depending on rainfall or snowfall levels. Finally, we suggest safety improvement measures based on the results of this study, which are expected to reduce the severity of traffic accidents in the future.

• Journal of Korean Society on Water Environment
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• v.26 no.4
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• pp.654-663
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• 2010

Attention has increasingly focused on the pollutant load discharged from rural area since the enforcement of total maximum daily loads (TMDLs) in korea. As one of the methods to control the inflow of pollutant load during wet weather events, local governments are attempting to apply non-point source control facility. To design those facilities appropriately, it is essential to understand the runoff characteristics of pollutants such as TSS, $BOD_5$, $COD_{Cr}$, TP and TN. In the paper, the quantitative analyses for pollutant runoff characteristics were examined in a small rural watershed with the area of about 53 hectares. For a dry weather day and wet weather events, variation patterns of dry weather flow and runoff characteristics of wet weather flows were monitored and investigated. The runoff model using XP-SWMM reflecting the landuse types of the watershed in detail was simulated to perform the sensitivity analyses for several factors influencing on their hydrograph and pollutographs. As a result, for the case of medium and small rainfall events (i. e. total rainfall of 35.8 and 17.5 mm), the impervious area including green house, roof and road which covers relatively low portion of total area (i. e. 16%) caused substantial first flush and the majority of total runoff load. Therefore, it has been concluded that the runoff characteristics of each pollutant and distribution of impervious area should be considered for the establishment of the control strategy of non-point pollutant runoff at a rural area.

• The Journal of Bigdata
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• v.2 no.1
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• pp.17-26
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• 2017

The gradual desertification in Northeastern China brought about by the industrial development and global warming, has affected the Korean peninsula as evident by the ultrafine dust geographically and seasonally. People with severe respiratory problems, senior citizens and the infants are susceptible to the ill effects of fine dust which could prove fatal to them. Hence, we need to study the root cause of fine dust emergence and the correlation verification between fine dust and its side effects. This study firstly analyzed clean and contaminated areas classified by industrial elements. We utilized air, weather and industrial data in the area. Next, we detected a change of fine dust in terms of weather and climate. We analyzed correlation of air and weather by influence from domestic and neighboring country. The result indicated that China is the culprit of the emergence of fine dust predicament. Consequently, we can prove that fine dust ($PM_{10}$) and ultrafine dust ($PM_{2.5}$) could arise from geographical, seasonal, and pollutant elements. Therefore, we propose the optimum to make countermeasures about fine dust in terms of industry, topography, population and living residence.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.158-169
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• 2004

A method for predicting the short-term dynamic line ratings in overhead transmission lines using real-time weather forecast data is proposed in this paper. Through some inspections for the 3-hour interval forecasting factors such as ambient temperature, wind speed grade and weather code given by KMA(Korea Meteorological Administration), correlation properties between forecast weather data and actual measured data are analyzed. To use these variable in determining the dynamic line ratings, they are changed into suitable numerical values. Furthermore adaptive neuro-fuzzy systems to improve reliabilities for wind speed and solar heat radiation ate designed It was verified that the forecast weather data can be used to predict the line rating with reliable. As a result it can be possible that the proposed predicting system can be effectively utilized by their anticipation a short-time in advance.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1122-1137
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• 2021

PM_2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.