• Fashion & Textile Research Journal
• /
• v.19 no.4
• /
• pp.434-447
• /
• 2017

This study investigated the effects of influencing factors on the sales volume of apparel products. Based on previous studies, weekend effect, discount rate, and meteorological factors including daily average temperature, rainfall, sea level pressure, and fine dust were selected as independent variables to calculate their effects on sales quantity of apparel products. The daily sales data during 2015 - 2016 were collected from casual brands and outdoor brands which "A" apparel manufacturing company had operated. The actual data of "A" company were analyzed using SAS(R) 9.4 and SAS(R) Enterprise Miner 14.1. The results of this study were as follows: First, the influencing factors on total sales volume of apparel products were proved to be the weekend effect, discount rate, and fine dust. Second, the analysis of influencing factors on sales volume of apparel products according to season showed: 1) In casual brands, the average temperature had a significant influence on the sales volume of spring/summer products, and the sea level pressure affected the sales volume of summer/fall/winter products significantly. 2) In outdoor brands, the average temperature and the fine dust had a significant influence on the sales volume of all season's products. The sea level pressure affected the sales volume of summer/fall/ winter products significantly. The weekend effect and the discount effect affected the sales volume of apparel products partly. Third, the effect of rainfall was not proven significant, which was different from the results of past studies.

• Korean Journal of Soil Science and Fertilizer
• /
• v.49 no.5
• /
• pp.420-428
• /
• 2016

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And $NDVI_{UAV}$ and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to $NDVI_{UAV}$ and other agro-meteorological factors were well reflected in the model.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.771-783
• /
• 2023

This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.647-659
• /
• 2017

Garlic and onion are grown in major cultivation regions that depend on the crop condition and the meteorology of the production area. Therefore, when yields are to be predicted, it is reasonable to use a statistical model in which both the crop and the meteorological elements are considered. In this paper, using a multiple linear regression model, we predicted garlic and onion yields in major cultivation regions. We used the MODIS NDVI that reflects the crop conditions, and six meteorological elements for 7 major cultivation regions from 2006 to 2015. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, the MODIS NDVI in February was chosen the significant independent variable of the garlic and onion yield prediction model. In the case of meteorological elements, the garlic yield prediction model were the mean temperature (March), the rainfall (November, March), the relative humidity (April), and the duration time of sunshine (April, May). Also, the rainfall (November), the duration time of sunshine (January), the relative humidity (April), and the minimum temperature (June) were chosen among the variables as the significant meteorological elements of the onion yield prediction model. MODIS NDVI and meteorological elements in the model explain 84.4%, 75.9% of the garlic and onion with a root mean square error (RMSE) of 42.57 kg/10a, 340.29 kg/10a. These lead to the result that the characteristics of variations in garlic and onion growth according to MODIS NDVI and other meteorological elements were well reflected in the model.

• Journal of Korea Water Resources Association
• /
• v.56 no.4
• /
• pp.261-272
• /
• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.

• Journal of the Korean earth science society
• /
• v.33 no.2
• /
• pp.113-125
• /
• 2012

The estimations of the surface rain intensity and rain-related physical variables derived from two independent Tropical Rainfall Measuring Mission (TRMM) satellite sensors, TRMM Microwave Imager (TMI) and Precipitation Radar (PR), were compared over four different oceans. The precipitating clouds developed most frequently in the warmest sea surface temperature (SST) region of the west Pacific, which is 1.5 times more frequent than in the east Pacific and the tropical Atlantic oceans. However, the east Pacific exhibited the most intense rain intensity for the convective and mixed rain types while the tropical Atlantic showed the most intense rain intensity for all TMI rainy pixels. It was found that the deviation of TMI-derived rain rate yielded a big difference in region-to-region and rain type-to-type if the PR rain intensity value is assumed to be closer to the truth. Furthermore, the deviation by rain types showed opposite signs between convective and non-convective rain types. It was found that the region-to-region deviation differences reached more than 200% even though the selected tropical oceans have relatively similar geophysical environments. Therefore, the validation for the microwave rain estimation needs to be performed according to both rain types and climate regimes, and it also requires more sophisticated TMI algorithm which reflects the locality of rainfall characteristics.

• Journal of Wetlands Research
• /
• v.20 no.1
• /
• pp.63-71
• /
• 2018

The particle filtration mechanisms in an infiltration trench should be varying due to the different hydraulic conditions during stormwater runoff. The understanding of these variations associated with different filtration mechanisms and their effect on the particle removal efficiency is of vital importance. Therefore, a LID (Low Impact Development) system comprising of an infiltration trench packed with gravel and woodchip was investigated during the monitoring of several independent rainfall events. A typical rainfall event was divided into separate regimes and their corresponding flow conditions as well as filtration mechanisms in the trench were analyzed. According to hydraulic conditions, it was found out that filtration changes between vertical and horizontal flows as well as between unsaturated, saturated, and partially-saturated flows. Particle separation efficiency was high (55-76%) and was mainly governed by physical straining during the unsaturated period. It was then enhanced by diffusion during the saturated period (75-95%). When the trench became partially saturated at the end of the rainfall event, the efficiency decreased which was believed to be due to the existence of a negatively charged air-water interface which limited the removal to positively charged particles.

• Journal of Korea Water Resources Association
• /
• v.40 no.2 s.175
• /
• pp.171-182
• /
• 2007

In this study, as a method for decreasing the confidence interval of the estimates of Clark hydrograph's concentration time and storage coefficient, regression equations of these parameters with respect to those of rainfall, meteorology, and basin characteristics are derived and analyzed using the Monte Carlo simulation technique. The results are also reviewed by comparing them with those derived by applying the Bootstrap technique and empirical equations. Results derived from this research are summarized as follows. (1) Even in case of limited rainfall events are available, it is possible to estimate the mean runoff characteristics by considering the affecting factors to runoff characteristics. (2) It is also possible to use the Monte Carlo simulation technique for estimating and evaluating the confidence intervals for concentration time and storage coefficient. The confidence intervals estimated in this study were found much narrower than those of Yoo et al. (2006). (3) A supporting result could also be derived using the Bootstrap technique. However, at least 20 independent rainfall events are necessary to get a rather significant result for concentration time and storage coefficient. (4) No empirical equations are found to be properly applicable for the study basin. However, empirical equations like the Kraven(I) and Kraven(II) are found valid for the estimation of concentration time, on the other hand the Linsley is found valid for the storage coefficient In this study basin. But users of these empirical formula should be careful as these also provide a wide range of possible values.

• The Research Journal of the Costume Culture
• /
• v.25 no.2
• /
• pp.117-129
• /
• 2017

The purpose of this study was to investigate meteorological factors' effects on clothing sales based on empirical data from a leading apparel company. The daily sales data were aggregated from "A" company's store records for the Fall/Winter season from 2012 to 2015. Daily weather data corresponding to sales volume data were collected from the Korea Meteorological Administration. The weekend effect and meteorological factors including temperature, wind, humidity, rainfall, fine dust, sea level pressure, and sunshine hours were selected as independent variables to calculate their effects on A company's apparel sales volume. The analysis used a SAS program including correlation analysis, t-test, and multiple-regression analysis. The study results were: First, the weekend effect was the most influential factor affecting sales volume, followed by fine dust and temperature. Second, there were significant differences in the independent variables'effects on sales volume according to the garments' classification. Third, temperature significantly affected outer garments'sales volume, while top garments' sales volume was not influenced significantly. Fourth, humidity, sea level pressure and sunshine affected sales volume partly according to the garments' item. This study can provide proof of significant relationships between meteorological factors and the sales volume of garments, which will serve well to establish better inventory strategies.

• Korean journal of applied entomology
• /
• v.25 no.4 s.69
• /
• pp.221-228
• /
• 1986

An empirical model to predict initial disease occurrence and subsequent progress of Alternaria leaf spot was constructed based on the modified degree day temperature and frequency of rainfall in three years field experiments. Climatic factors were analized 10-day bases, beginning April 20 to the end of August, and were used as variables for model construction. Cumulative degree portion (CDP) that is over $10^{\circ}C$ in the daily average temperature was used as a parameter to determine the relationship between temperature and initial disease occurrence. Around one hundred and sixty of CDP was needed to initiate disease incidence. This value was considered as temperature threshhold. After reaching 160 CDP, time of initial occurrence was determined by frequency of rainfall. At least four times of rainfall were necessary to be accumulated for initial occurrence of the disease after passing temperature threshhold. Disease progress after initial incidence generally followed the pattern of frequency of rainfall accumulated in those periods. Apparent infection rate (r) in the general differential equation dx/dt=xr(1-x) for individual epidemics when x is disease proportion and t is time, was a linear function of accumulation rate of rainfall frequency (Rc) and was able to be directly estimated based on the equation r=1.06Rc-0.11($R^2=0.993$). Disease severity (x) after t time could be predicted using exponential equation $[x/(1-x)]=[x_0/(1-x)]e^{(b_0+b_1R_c)t}$ derived from the differential equation, when $x_0$ is initial disease, $b_0\;and\;b_1$ are constants. There was a significant linear relationship between disease progress and cumulative number of air-borne conidia of Alternaria mali. When the cumulative number of air-borne conidia was used as an independent variable to predict disease severity, accuracy of prediction was poor with $R^2=0.3328$.