• Atmosphere
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• v.19 no.2
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• pp.199-211
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• 2009

In this paper, we examine the multiple change-point and change pattern in the 54 years (1954-2007) time series of the annual and the heavy precipitation characteristics (amount, days and intensity) averaged over South Korea. A Bayesian approach is used for detecting of mean and/or variance changes in a sequence of independent univariate normal observations. Using non-informative priors for the parameters, the Bayesian model selection is performed by the posterior probability through the intrinsic Bayes factor of Berger and Pericchi (1996). To investigate the significance of the changes in the precipitation characteristics between before and after the change-point, the posterior probability and 90% highest posterior density credible intervals are examined. The results showed that no significant changes have occurred in the annual precipitation characteristics (amount, days and intensity) and the heavy precipitation intensity. On the other hand, a statistically significant single change has occurred around 1996 or 1997 in the heavy precipitation days and amount. The heavy precipitation amount and days have increased after the change-point but no changes in the variances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.132-132
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• 2021

Access to accurate spatial precipitation in many hydrological studies is necessary. Existence of many mountains with diverse topography in South Korea causes different spatial distribution of precipitation. Rain gauge stations show accurate precipitation information in points, but due to the limited use of rain gauge stations and the difficulty of accessing them, there is not enough accurate information in the whole area. Weather radars can provide an integrated precipitation information spatially. Despite this, weather radar data have some errors that can not provide accurate data, especially in heavy rainfall. In this study, some location-based variable like aspect, elevation, plan curvature, profile curvature, slope and distance from the sea which has most effect on rainfall was considered. Then Automatic Weather Station data was used for spatial training of variables in each event. According to this, K-fold cross-validation method was combined with Adaptive Neuro-Fuzzy Inference System. Based on this, 80% of Automatic Weather Station data was used for training and validation of model and 20% was used for testing and evaluation of model. Finally, spatial distribution of precipitation for 1×1 km resolution in Gwangdeoksan radar station was estimates. The results showed a significant decrease in RMSE and an increase in correlation with the observed amount of precipitation.

• The Journal of Engineering Geology
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• v.18 no.1
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• pp.1-6
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• 2008

Precipitation and groundwater level data sets from Kum river watershed were analyzed and compared. The correlation between groundwater level and the moving average of precipitation was analyzed. Moving averaging technique is stochastic method and that was used to consider the effect of precipitation events on groundwater level fluctuation. Groundwater level generally follows seasonal precipitation pattern and low level occurs from early December to late April. Relatively high groundwater level is appeared in wet spell (July and August). The correlation between groundwater level and the moving average of precipitation to consider precedent precipitation events was analyzed with minimum two-year data sets. When the precipitation and groundwater level data set pair was selected the precipitation gauge station is closely located to groundwater level gauge station in the upstream direction to minimize the non-homogeneous precipitation distribution effect. The maximum correlation was occurred when the averaging periods were from 10 days to 150 days with Kum river watershed data. The correlation coefficients are influenced by data quality, missing data periods, or snow melt effect, etc. The maximum coefficient was 0.8886 for Kum river watershed data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.143-157
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• 1987

In order to determine the design precipitation, the most probable daily precipitation and annual precipitation at every spot are calculated and iso - precipitation line are drawn. Probability of precipitation and drought phenomena of each gage station are analyzied by the method of frequency analysis from the statistical conceptions. The results summarized in this study are as the follows. 1. Annual mean precipitation in kyungpook area are 1044 mm, about 115 mm less than annual mean precipitation of Korea amounts to l1S9mm, and found to regionally unequal. 2. Monthly mean rainfall of July is 242.2mm, 23.2%, August 174.2mm, 16.7%, June 115mm, 11% and September 114.2mm, 10.9% and Rainfall depth of July-August are more than 40% of annual precipition. This shows notable summer rainy weather by typoon and low pressure storm and seasonal unbalance of water supply. 3. The relation among the maximum precipi.tation per day, per two continuous days and per three contnous days are caculated and the latter is found 31.0% increased rate of the first and the last 48.2% increased rate of first. 4. Probability precipitation in Kyungpook area are shown as 9.0%(5 year), 13.3%(10 year), 17.7%(20 year), 23.1%(50 year), 27.0%(100 year) and 31.1%(200 year) increased rate of each recurrence year compared with observed average annual precipitation. 5. From annual precipitation and maximum daily rainfall data probability of precipitation and precipitation isohyetal line are derived which shown as Table 11 and Fig. 8. 6. Drought days are divided 6 class and analysed results are shown on table 12. Average occurrence time of 10-14 continuous drought days are 2.3 time per year, 15-19 days are 0.9 time per year, 20-24 days are one per six years, 30-34 days are once per nine years and over than 35days are once per 25 years.

• Journal of the Korean Ceramic Society
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• v.26 no.3
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• pp.423-437
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• 1989

Al2O3/ZrO2 composites were prepared by precipitation method using Al2(SO4)3.18H2O, ZrOCl2.8H2O and YCl3.6H2O as starting materials and NH4OH as a precipitation agent. Al2O3/ZrO2 composites(series A) were prepared by mixing Al2O3 powder obtained by single precipitation method with ZrO2(＋3m/o Y2O3) powder obtained by co-predipitation method. Al2O3/ZrO2 composites (series B) were prepared by co-precipitation method using the three starting materials. In all cases, the composition was controlled as Al2O3-15v/o ZrO2(＋3m/o Y2O3). The composites of series A showed higher final relative densities than those of series B and tetagonal ZrO2 in all cases was retained to about 95% at room temperature. ZrO2 particles were coalesced more rapidly in grain boundary of Al2O3 than within Al2O3 grain. ZrO2 particles were located at 3-and 4-grain junction of Al2O3 and limited the grain growth of Al2O3. It was observed that MgO contributed to densification of Al2O3 but limited grain growth of Al2O3 by MgO was not remarkable. In all Al2O3/ZrO2 composites, exaggerated grain growth of Al2O3 was not observed and Al2O3/ZrO2 composites were found to have homogeneous microstructures.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.253-268
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• 2020

In this study, recharge rates are estimated using SWAT-K (a distributed hydrological model). The validity of the estimated recharge rates were evaluated by employing the baseflow separation method based on observed hydrological data. The exploitable groundwater is typically determined as the 10-year drought frequency recharge rate that is calculated by average recharge ratio multiplied by 10-year drought frequency precipitation. In practice, however, recharge rates typically decrease in line with precipitation; therefore, exploitable groundwater could be overestimated when average recharge rates are used without considering precipitation. To resolve this overestimation, exploitable groundwater was calculated by re-estimating recharge rates that consider precipitation intensity. By applying this method to the Uiwang, Gwacheon, and Seongnam sub-basins, the exploitable groundwater decreased by 55.5~77.6%, compared with recharge rates obtained using the existing method.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.257-264
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• 2010

The amount and continuity of precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study a linear programming method treated as a data-driven approach for estimating the missing rainfall data is compared with seven other methods widely used and its superiority is certified. The data used in this research are annual precipitation ones during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station are estimated and the areal averages of annual precipitation data for 32 years at the Han River basin are calculated.

• Korean Journal of Materials Research
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• v.26 no.5
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• pp.235-240
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• 2016

Appropriate thermo-mechanical properties of nickel-based superalloys are achieved by heat treatment, which induces precipitation and solid solution hardening; thus, information on the temperature ranges of precipitation and dissolution of the precipitates is essential for the determination of the heat treatment condition. In this study, thermal analyses of nickel-based superalloys were performed by differential scanning calorimetry method under conditions of various heating rates of 5, 10, 20, or 40K/min in a temperature range of 298~1573K. Precipitation and dissolution temperatures were determined by measuring peak temperatures, constructing trend lines, and extrapolating those lines to the zero heating rate to find the exact temperature under isothermal condition. Determined temperatures for the precipitation reactions were 813, 952, and 1062K. Determined onset, peak, and offset temperatures of the first dissolution reaction were 1302, 1388, and 1406K, respectively, and those values of the second dissolution reaction were 1405, 1414, and 1462K. Determined solvus temperature was 1462K. The study showed that it was possible to use a simple method to obtain accurate phase transition temperatures under isothermal condition.