• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• Water for future
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• v.24 no.3
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• pp.71-82
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• 1991

Methods of predicting water resources availiability of a river basin can be classified as empirical formula, water budget analysis and regression analysis. The purpose of this study is to develop a method to estimate the monthly runoff required for long-term water resources development project. Using the monthly runoff data series at gaging stations alternative multiple regression models were constructed and evaluated. Monthly runoff volume along with the meteorological and physiographic parameters of 48 gaging stations are used, those of 43 stations to construct the model and the remaining 5 stations to verify the model. Regression models are named to be Model-1, Model-2, Model-3 and Model-4 developing on the way of data processing for the multiple regressions. From the verification, Model-2 is found to be the best-fit model. A comparison of the selected regression model with the Kajiyama's formula is made based on the predicted monthly and annual runoff of the 5 watersheds. The result showed that the present model is fairly resonable and convinient to apply in practice.

• Wind and Structures
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• v.19 no.4
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• pp.421-441
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• 2014

With the assistance of typhoon field data at aerial elevation level observed by meteorological satellites and wind velocity and direction records nearby the ground gathered in Guangzhou Weather Station between 1985 and 2001, some key wind field parameters under typhoon climate in Guangzhou region were calibrated based on Monte-Carlo stochastic algorithm and Meng's typhoon numerical model. By using Peak Over Threshold method (POT) and Generalized Pareto Distribution (GPD), Wind field characteristics during typhoons for various return periods in several typical engineering fields were predicted, showing that some distribution rules in relation to gradient height of atmosphere boundary layer, power-law component of wind profile, gust factor and extreme wind velocity at 1-3s time interval are obviously different from corresponding items in Chinese wind load Codes. In order to evaluate the influence of typhoon field parameters on long-span flexible bridges, 1:100 reduced-scale wind field of type B terrain was reillustrated under typhoon and normal conditions utilizing passive turbulence generators in TJ-3 wind tunnel, and wind-induced performance tests of aero-elastic model of long-span Guangzhou Xinguang arch bridge were carried out as well. Furthermore, aerodynamic admittance function about lattice cross section in mid-span arch lib under the condition of higher turbulence intensity of typhoon field was identified via using high-frequency force-measured balance. Based on identified aerodynamic admittance expressions, Wind-induced stochastic vibration of Xinguang arch bridge under typhoon and normal climates was calculated and compared, considering structural geometrical non-linearity, stochastic wind attack angle effects, etc. Thus, the aerodynamic response characteristics under typhoon and normal conditions can be illustrated and checked, which are of satisfactory response results for different oncoming wind velocities with resemblance to those wind tunnel testing data under the two types of climate modes.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.83-91
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• 2021

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.3
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• pp.334-340
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• 2019

Objective: The objective of this study was to investigate the genetic components of daily milk yield and to re-rank bulls in South Korea by estimated breeding value (EBV) under heat stress using the temperature-humidity index (THI). Methods: This study was conducted using 125,312 monthly test-day records, collected from January 2000 to February 2017 for 19,889 Holstein cows from 647 farms in South Korea. Milk production data were collected from two agencies, the Dairy Cattle Genetic Improvement Center and the Korea Animal Improvement Association, and meteorological data were obtained from 41 regional weather stations using the Automated Surface Observing System (ASOS) installed throughout South Korea. A random regression model using the THI was applied to estimate genetic parameters of heat tolerance based on the test-day records. The model included herd-year-season, calving age, and days-in-milk as fixed effects, as well as heat tolerance as an additive genetic effect, permanent environmental effect, and direct additive and permanent environmental effect. Results: Below the THI threshold (${\leq}72$; no heat stress), the variance in heat tolerance was zero. However, the heat tolerance variance began to increase as THI exceeded the threshold. The covariance between the genetic additive effect and the heat tolerance effect was -0.33. Heritability estimates of milk yield ranged from 0.111 to 0.176 (average: 0.128). Heritability decreased slightly as THI increased, and began to increase at a THI of 79. The predicted bull EBV ranking varied with THI. Conclusion: We conclude that genetic evaluation using the THI function could be useful for selecting bulls for heat tolerance in South Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.5
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• pp.599-607
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• 2015

The purpose of this study is to reproduce long-term wave fields in coastal waters of Korea based on wave hind-casting modelling and discuss its applications. To validate wind data(NCEP, ECMWF, JMA-MSM), comparison of wind data was done with wave buoy data. JMA-MSM predicted wind data with high accuracy. But due to relatively longer period of ECMWF wind data as compared to that of JMA-MSM, wind data set of ECMWF(2001~2014) was used to perform wave hind-casting modelling. Results from numerical modelling were verified with the observed data of wave buoys installed by Korea Meteorological Administration(KMA) and Korea Hydrographic and Oceanographic Agency(KHOA) on offshore waters. The results agree well with observations at buoy stations, especially during the event periods such as a typhoon. Consequently, the wave data reproduced by wave hind-casting modelling was used to obtain missing data in wave observation buoys. The obtained missing data indicated underestimation of maximum wave height during the event period at some points of buoys. Reasons for such underestimation may be due to larger time interval and resolution of the input wind data, water depth and grid size etc. The methodology used in present study can be used to analyze coastal erosion data in conjunction with a wave characteristic of the event period in coastal areas. Additionally, the method can be used in the coastal disaster vulnerability assessment to generate wave points of interest.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1119-1130
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• 2021

Urban flooding caused by localized heavy rainfall with unstable climate is constantly occurring, but a system that can predict spatial flood information with weather forecast has not been prepared yet. The worst flood situation in urban area can be occurred with difficulties of structural measures such as river levees, discharge capacity of urban sewage, storage basin of storm water, and pump facilities. However, identifying in advance the spatial flood information can have a decisive effect on minimizing flood damage. Therefore, this study presents a methodology that can predict the urban flood map in real-time by using rainfall data of the Korea Meteorological Administration (KMA), the results of two-dimensional flood analysis and random forest (RF) regression model. The Ujeong district in Ulsan metropolitan city, which the flood is frequently occurred, was selected for the study area. The RF regression model predicted the flood map corresponding to the 50 mm, 80 mm, and 110 mm rainfall events with 6-hours duration. And, the predicted results showed 63%, 80%, and 67% goodness of fit compared to the results of two-dimensional flood analysis model. It is judged that the suggested results of this study can be utilized as basic data for evacuation and response to urban flooding that occurs suddenly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Journal of the Korean earth science society
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• v.38 no.7
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• pp.481-495
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• 2017

The purpose of this study is to investigate and analyze the relationship between meteorologicalclimatic factors and fruit property data from Aronia sampling points during May to August 2016 in the Danyang area. For this purpose, we investigated the meteorological factor, the physicalchemical property of fruit and soil, and the property change of fruit according to the setting of rain and daylight shielding from Aronia sampling points. The result indicate that first, meteorologicalclimatic factors such as the maximum air temperature, the accumulated precipitation, the relative humidity, and daylight hours are a positive influence on products and maintenance of quality of Aronia as well as a suitable field for cultivating Aronia in the Danyang. However, a strong wind in April and May deeply affects the falling phenomenon of the flowering and blooming season. Second, the quality and products of Aronia show the high correlation coefficients of more than 0.9 with agricultural meteorologicalclimatic factors such as daily maximum temperature, daily soil temp, daily soil pH, cumulated precipitation, and daily soil humidity. Also, they can be predicted by the regression equations using these factors. Third, it is necessary to maintain the rain shielding in these fields because antocyanin and saccharinity components within Aronia decreased in case of heavy rainfalls. And, the result of regression analysis saccharinity and antocyanin within aronia from normal fields and rain shieldingfields at Aronia sampling points show a high correlation, respectively.