• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.251-264
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• 2008

In this study, a nonhomogeneous markov model which is able to simulate hourly rainfall series is developed for estimating reliable hydrologic variables. The proposed approach is applied to simulate hourly rainfall series in Korea. The simulated rainfall is used to estimate the design rainfall and flood in the watershed, and compared to observations in terms of reproducing underlying distributions of the data to assure model's validation. The model shows that the simulated rainfall series reproduce a similar statistical attribute with observations, and expecially maximum value is gradually increased as number of simulation increase. Therefore, with the proposed approach, the non-homogeneous markov model can be used to estimate variables for the purpose of design of hydraulic structures and analyze uncertainties associated with rainfall input in the hydrologic models.

• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.111-121
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• 2010

DHV (Design-Hour Volume) for the estimation of number of lanes is determined by design-hour factor (K). The design-hour factor is defined as the proportion between the 30th highest hourly volume and AADT and determines the level of road planning. However, the K-factor estimated by an existing method has a problem because the hourly volumes on holiday and weekend appear in the relatively low rank in real world in spite of expected high volumes. To improve this problem, this study make use of the concept of traffic demand in estimating the design-hour factor. After the congested hourly volumes transfer to traffic hourly demand, the K-factors are estimated on urban expressways and are compared to the existing K-factors. It is perceived that the new K-factors have more realistic values due to utilizing the traffic demand. reflecting the congested flow.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• ETRI Journal
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• v.37 no.2
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• pp.283-294
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• 2015

A model that precisely forecasts how much wind power is generated is critical for making decisions on power generation and infrastructure updates. Existing studies have estimated wind power from wind speed using forecasting models such as ANFIS, SMO, k-NN, and ANN. This study applies a projected clustering technique to identify wind power patterns of wind turbines; profiles the resulting characteristics; and defines hourly and daily power patterns using wind power data collected over a year-long period. A wind power pattern prediction stage uses a time interval feature that is essential for producing representative patterns through a projected clustering technique along with the existing temperature and wind direction from the classifier input. During this stage, this feature is applied to the wind speed, which is the most significant input of a forecasting model. As the test results show, nine hourly power patterns and seven daily power patterns are produced with respect to the Korean wind turbines used in this study. As a result of forecasting the hourly and daily power patterns using the temperature, wind direction, and time interval features for the wind speed, the ANFIS and SMO models show an excellent performance.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.4
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• pp.429-439
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• 1999

In order to assess the chronic impact of tropospheric ozone on vegetation in the Seoul metropolitan area, it is necessary to quantify ozone exposure. Two ozone indices commonly used to relate ozone exposure to injury of vegetation were calculated. SUM06(SUM of hourly concentrations at or above 0.06 ppm) and AOT40(Accumulated exposure Over a Threshold of 40 ppb) which are widely used as ozone indices in the US and Europe were calculated based on hourly ozone concentrations in 5 areas of Seoul and 5 cities of Kyunggido during 1990~1997. Most SUM06 levels were 1~5ppm.hr, however several areas in Northern and Eastern Seoul reached about 5~7 ppm.hr in 1996~1997. AOT40 values were as high as 17~24 ppm.hr. Although measured SUM06 levels would not be expected to significantly impact vegetation, the overall ozone index, as well as annual average, 95th, and 99th percentile have increased continuously over the last 8 years. Often, ozone concentrations are lower in cities where there is a significant NOx concentration, than in outlying rural agricultural areas where NOx scrubbing is not as important. Concentrations greater than 40 ppb, which can cause chronic ozone toxicity to vegetation, were found mostly in the summer and constitutued about 5~15% of total hourly ozone cocentrations.

• Journal of Environmental Science International
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• v.6 no.5
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• pp.461-472
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• 1997

This study was carried out to investigate the characteristics of chemical components and precipitation at Kimhae area from March, 1992 to June, 1994. The pH values, concentration of soluble ions($Cl^-$, $NO_2^-}$ $NO_3^-}$, $NO_4^{2-}$-, $PO_4^{3-}$. $F^-$, $Mg^{2+}$, $Ca^{2+}$, $Mn^{2+}$, $K^+) and non-soluble metals(Cr.Si. Zn, Pb, Cu, Fe, Mn, Mg, Ad. V. Cal were measured by pH meter, IC (ion Chromatography) and ICP(Inductively Coupled Plasma). The data were analyzed by the dally. hourly distribution characteristics of acidity and chemical components, as well as the correlation between them. The results are as follows. 1. The pH range of precipitation was from 3.45 to 6.80 in Kimhae area. and average value was pH 4.62 and main chemical components were $SO_4^{2-}$, $Cl^-$, $NO_3^-$. The highest pH value and concentration appeared in initial rain, which might result from urbanlzation and industrialization in this area and long term transportation from China. 2. The hourly correction distribution of main anions related to pH value In the rainwater showed $SO_4^{2-}$ > $NO_3^-$ > $Cl^-$. Hourly concentration of heavy metal and each ion was highly correlated with pH in the precipitation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1041-1045
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• 2010

Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.76.1-76.1
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• 2011

With the growing penetration of distributed generation including from renewable sources, smart grid power system is needed to address the reliability problem. One important feature of smart grid is demand response. In order to design a demand response program, it is indispensable to understand how consumer reacts upon the change of electricity price. In this paper, we construct an econometrics model to estimate the hourly price elasticity of demand. This panel model utilizes the hourly load data obtained from KEPCO for the period from year 2005 to 2009. The hourly price elasticity of demand is found to be statistically significant for all the sample under investigation. The samples used for this analysis is from the past historical data under the price structure of three different time zones for each season. The result of the analysis of this time of use pricing structure would allow the policy maker design an appropriate incentive program. This study is important in the sense that it provides a basic research information for designing future demand response programs.

• Journal of Korean Society for Atmospheric Environment
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• v.17 no.3
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• pp.277-288
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• 2001

Continuous atmospheric CO$_2$measurements measured during the 1 year(1998.8∼1999.8) at Korea Global Atmosphere Watch Observatory (KGAWO) in Anmyeon-do are analyzed by the selecting method which is recommended by WDCGG to get background CO$_2$. This method can reject data based on two criteria: the instability of CO$_2$ concentration within 1 hour from hourly standard deviation (hourly variability$\leq$ 0.6 ppm first selection) and the large changes in the CO$_2$ concentration from one hour to the nex(∼$\leq$0.3 ppm, second selection). We could obtain hourly background CO$_2$ of 37% in first selection and 20% in second selection during the l year. That are a little less than those of Ryori station in Japan. especially, the cases of background CO$_2$ which is selected were few during the summer. That is caused by affection of vegetation and anthropogenic source. After the selecting methods are applied, the cases which is selected for easterly wind decrease remarkably according to the analysis of wind direction about continuous CO$_2$ .That was affected by anthropogenic source from the east area.