• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.600-605
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• 2004

The road construction with horizontal expansion of country using and augmentation of traffic demand is advanced actively and it accompanies hereupon, the above of 70% of the country is formed at the mountain in our country where the hazard cut slope has been created. In this study, It is prepared a effective management countermeasure of cut slope introduced priority investigation decision method against hazard cut slope which is influenced by abnormally rainfall by an unusual change in the weather such as a guerilla rainfall character. In meaning link, It was executed collapse cause by failure character analysis in the cut slope which has failed for the last five years and it is prepared the hazard grade criterion from E to A grade according to collapse cause. It is decided that a maintenance management grade by the hazard grade classification criterion of cut slope. So It is possible to hazard cut slope. It is established failure protection counter countermeasure by effective maintenance management through the hazard grade c1assification criterion and it will be able to dispose to advanced nation level like Hong Kong and Japanese.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.578-582
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• 2005

In this study, the conceptual foundation and development process of the Antecedent soil Moisture Condition(AMC) in SCS runoff curve number method are reviewed. Although the runoff volume is very sensitive with AMC condition, the AMC class limits developed in SCS(1972) are used in rainfall-runoff analysis without careful consideration. Tn this study, following the SCS curve number development process, rainfall-runoff characteristics of the Jang-Pyung subbasin subject to the Pyung-Chang River basin are analyzed to evaluate the reasonability of the AMC class limits at present. The New AMC class limits are proposed by the sensitive analysis of the antecedent rainfall - curve number value. As a result, the classification value of AMC-I with II is 22mm of antecedent 5-day rainfall amount, and the classification of AMC-II with III is 117mm in growing season. When the New AMC class limits are applied to Jang-Pyung subbasin, AMC probability distribution shows that the AMC-II has increased remarkably even though the AMC-I has a little higher value. But the AMC-III has the smallest one. According to the conceptual basis of the curve number method, the AMC probability distribution, the New AMC class limits adopted, gives reasonable results.

• Atmosphere
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• v.14 no.1
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• pp.24-43
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• 2004

This paper describes the detailed characteristics of heavy rainfall events occurred in Chungcheong province on 15 and 16 April and from 6 to 8 August 2002 based on the analysis of raingauge rainfall rate and radar reflectivity from the METRI's X-band Weather Radar located in Cheongju. A synoptic analysis of the case is carried out, first, and then the analysis is devoted to seeing how the radar observes the case and how much information we obtain. The highly resolved radar reflectivity of horizontal and vertical resolutions of 1 km and 500 m, respectively shows a three-dimensional structure of the precipitating system, in a similar sequence with the ground rainfall rate. The radar echo classification algorithm for convective/stratiform cloud is applied. In the convectively-classified area, the radar reflectivity pattern shows a fair agreement with that of the surface rainfall rate. This kind of classification using radar reflectivity is considered to be useful for the precipitation forecasting. Another noteworthy aspect of the case includes the effect of topography on the precipitating system, following the analysis of the surface rainfall rate, topography, and precipitating system. The results from this case study offer a unique opportunity of the usefulness of weather radar for better understanding of structural and variable characteristics of flash flood-producing heavy rainfall events, in particular for their improved forecasting.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.96-96
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• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• Journal of the Korean Society of Costume
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• v.52 no.2
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• pp.139-150
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• 2002

The purpose of this study was to explore the effects of meteorological factors on sales of apparel products. Basic fiat came out daily meteorological data and sales data of apparel products in department store from 1998 to 2000. Four factors(the average temperature, rainfall, wind velocity, sunshine duration) from the nine meteorological factors were selected and were collected with Korea Meteorological Administration. Sales data were collected with business strategy department of H (department store in Seoul. The sales data were divided into six classifications, which are woman's wear, men's wear, children's wear, golf wear, sports wear, and inner wear. The results of this study were as follows: 1) Sales of apparel products were significantly correlated with the average temperature, rainfall, wind velocity, sunshine duration. Among the meteorological factors, temperature turned out to be the most influential in apparel sales and then the amount of rainfall, sunshine duration affected sales according to apparel classifications differently. 2) There were some differences among the apparel classifications in the effect of meteorological factors on the sales of apparel. In the spring. the higher the temperature was, the higher the sales of women's wear and golf wear were, but the lower the sales of children's wear, sports wear and inner wear were. In the summer, The higher the amount of rainfall was, the lower the sales of all the apparel classification were. The higher the temperature was, the higher the sales of sports wear were. In the fall, the lower the temperature was, the higher the sales of all the apparel classification except snorts wear were. In the winter, the meteorological factors had little effect on the sales of women's wear, men's wear and children's wear. The higher the temperature was, the higher the sales of golf wear were. The lower the temperature was, the higher the sales of sports wear were.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.33-40
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• 2023

Machine-learning techniques are discovering effective performance on data analytics. Classification and regression are supported for prediction on different kinds of data. There are various breeds of classification techniques are using based on nature of data. Threshold determination is essential to making better model for unlabelled data. In this paper, threshold value applied as range, based on min-max normalization technique for creating labels and multiclass classification performed on rainfall data. Binary classification is applied on autism data and classification techniques applied on child abuse data. Performance of each technique analysed with the evaluation metrics.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.6
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• pp.439-446
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• 2011

In this research, enhanced land-cover classification methods using high-resolution satellite image (HRSI) and GIS in terms of practicality and accuracy was proposed. It aims for understanding non-point pollutant origin/loading, assessment the efficiency of rainfall storage/infiltration facilities and sounds water-environment management. The result of applying enhanced land-cover classification methods to the urban region verifies that roof and road area are including various vegetations such as roof garden, flower bed in the median strip and street tree. This accounts for 3% of total study area, and more importantly it was counted as impervious area by GIS alone or conventional indoor work. The feasibility of the method was assessed by applying to rainfall-runoff analysis for three weak rainfall in the range of 7.1-10.5 mm events in 2000, Chiba, Japan. A good agreement between simulated and observed runoff hydrograph was obtained. In comparison, the hydrograph simulated with land-use parameters by the detailed land-use information of 10m grid had an error between 31%~71%, while enhanced method showed 4% to 29%, and showed the improvement particularly for reproducing observed peak and recession flow rate of hydrograph in weak rainfall condition.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1029-1038
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• 2010

In meteorology Korea has 2/3 of rain of annual total rainfall at the month of Jun through Sept and it has possibility to have serious flood damage because geographically it is composed of mountainous area with steep slope which account for 70% of its country. Also, the increase of impervious layer due to industrialization and urbanization causes direct runoff, which deteriorates contamination of rivers by moving the contaminated material on the surface at the beginning of rain. In particular, the area of leisure facilities needs the management of water quality absolutely because dense population requires space of park function and place to relax and increases moving capability of non-point pollution source. For disposition of rainfall & runoff, the standard of initial rainfall, which is to be used for the computation of disposition volume, is significant factors for the runoff study of non-point pollution source, Until now, a great deal of study has been done by many researchers. However, it is the current reality that the characteristics of runoff varies according to land protection comprising river basin and the standard of initial rainfall by each researcher is not clearly defined yet. Therefore, in this research, it is suggested that, with the introduction of SOM (Self-Organizing Map), the standard of initial rainfall be determined after analyzing each sectional data by executing pattern classification about runoff and water quality data measured at the test river basin for this research.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.995-1007
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• 2008

Studies on modeling the rainfall-runoff relationship which shows nonlinear trend strongly use artificial neural networks theory not only for the prediction but also for the characteristics analysis of the data used by pattern classification. For the pattern classification, the results from Self-Organizing Map (SOM) mention that the map size and array for the SOM training have significantly influenced on the SOM performance. Since there is no deterministic method or theoretical equation to determine the number of rows and columns for the map size, hexagonal array is generally used for the map array. Therefore, this study present a determination of the optimized map structure for the rainfall-runoff analysis in Naju station considering the map size and array simultaneously which can represent the classified characterization of rainfall-runoff relationship. The result showed that the map size of 20$\times$16 hexagonal array with 8-clustered patterns was selected as an appropriate map structure for rainfall-runoff analysis in Naju station.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.