• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.115-125
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• 2012

The comparison between the spatial and temporal variability of aridity index, precipitation effectiveness and runoff during El-Nino and La-Nina periods and that of the normal period was conducted to evaluate the regional impacts of El-Nino, La-Nina in hydrologic variables. Aridity index and precipitation effectiveness were estimated using 59 nationwide weather stations data and runoff data of WAMIS were used. The ratio of the difference between El-Nino, La-Nina year value and that of normal year was analyzed. Temporal variation demonstrated that aridity index, precipitation effectiveness and run-off discharge increase in March, April, August, November, December and decrease in February, June, September, October according to El-Nino effect. Aridity index, precipitation effectiveness and run-off discharge increase in March, May, September and decrease in June, August, November, December according to La-Nina effect. The spatial variation of those variables analyzed for different basins showed that impacts in the Han river basin relatively higher than that of other basins.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.4
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• pp.314-339
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• 1982

Fluctuating climate is still most important environmental constrain, although improved modem agricultural technology has succeeded to increase crop production in the world. To stabilize the food production under fluctuating weather conditions, it is very needed to obain the quantitative information of interactions between crops and climate. The main purpose of this paper is three hold. Using the JIBP-data, the dry matter accumulation of rice crops is studied in relation to weather indexes (\SigmaTa and \SigmaSt). Temperature dependence of the yield index of rice is analyzed as to air temperature and water temperature. \SigmaT$_{10}$ -fluctuations are studied using meteorological data at various stations. The possible shift of \SigmaT$_{10}$ -isopleths due to climate fluctuation is evaluated. The second interest is in the plant climate of rice crops. Using results of canopy photosynthesis, it is pointed that the canopy structure has most important implication in plant climate. Leaf-air, stomatal, and mesophyll resistances of rice crops are described in relation to weather conditions. The change in light condition and aerodynamical property of rice crops with the growth is illustrated. The energy partition is also studied at different growing stages. Third point is to show in more detail effective countermeasures against cold irrigation water and cool summer. Heat balance of warming pond and polyethylene tube as a heat exchanger is studied to make nomo-grams for evaluating the necessary area and necessary length. Effects of windbreak net on rice crops are illustrated by using experimental and simulation results.lts.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.901-922
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• 2007

To see how the electricity demand for air-conditioning responds to weather condition and what kind of weather condition works better in forecasting maximum daily electricity demand, four different regression models, which are linear, exponential, power and S-curve, are adopted. The regression outcome turns out that the electricity demand for air-conditioning is inclined to rely on the exponential model. Another major discovery of this study is that the electricity demand for air-conditioning responds more sensitively to the weather condition year after year along with the higher non-air-conditioning electricity demand. In addition, it has also been found that the discomfort index explains the electricity demand for air-conditioning better than the highest temperature.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.2
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• pp.304-313
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• 2012

Uncertainty increases as a result of environment change and change of individual decision-making in extreme weather. This study consider individual decision-making which has been not covered until now. The purpose of this study is making Agent-Based Model to predict it more accurate that how much change travel demand in heavy rain and heavy snow. Through this model, it can be utilized to forecast travel demand, changes in travel behavior and traffic patterns. It will be also possible to predict discomfort index and risk of accidents.

• Journal of Ocean Engineering and Technology
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• v.35 no.1
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• pp.13-23
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• 2021

Since the introduction of the mandatory energy efficiency design index (EEDI), several studies have been conducted on the maneuverability of waves owing to the decrease in engine power. However, most studies have used the mean wave force during a single cycle to evaluate maneuverability and investigated the turning performance. In this study, we calculated the external force in accordance with the angle of incidence of the wave width and wavelengths encountered by KVLCC2 (KRISO very large crude-oil carrier) operating at low speeds in regular waves using computational fluid dynamics (CFD). We compare the model test results with those published in other papers. Based on the external force calculated using CFD, an external force that varies according to the phase of the wave that meets the hull was derived, and based on the derived external force and MMG control simulation, a maneuvering simulation model was constructed. Using this method, a weather vaning simulation was performed in regular waves to evaluate the course-keeping ability of KVLCC2 in waves. The results confirmed that there was a difference in the operating trajectory according to the wavelength and phase of the waves encountered.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.192-193
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• 2015

Construction Cycle Trend Survey, which survey total value of orders and realized amounts monthly, is a valuable statistics that used to quick grasp or forecast the trend of domestic construction business. In recent periodical survey quality diagnoses, few professional users named a problem that Construction Cycle Trend Survey could not get together with the current state of the construction industry. This study examined weather Construction Cycle Trend Survey reflects the economic sentiment of construction business or not. Paired t test was performed between Construction Cycle Trend Survey and Construction Business Survey Index (CBSI), and significant differences were verified.

• Journal of Astronomy and Space Sciences
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• v.20 no.2
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• pp.101-108
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• 2003

This study reports one approach for the classification of magnetic storms into recurrent patterns. A storm event is defined as a local minimum of Dst index. The analysis of Dst index for the period of year 1957 through year 2000 has demonstrated that a large portion of the storm events can be classified into a set of recurrent patterns. In our approach, the classification is performed by seeking a categorization that minimizes thermodynamic free energy which is defined as the sum of classification errors and entropy. The error is calculated as the squared sum of the value differences between events. The classification depends on the noise parameter T that represents the strength of the intrinsic error in the observation and classification process. The classification results would be applicable in space weather forecasting.

• Journal of the Korean Solar Energy Society
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• v.27 no.4
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• pp.67-75
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• 2007

This study intends to the adequacy inspection of the room temperature variation rate that is available in the building heat performance evaluation index, so we performed the sensitivity analysis about the room temperature variation rate and the energy consumption in the room. For these purpose, we supposed the models which are composed of the various window area, insulation thickness and ventilation rate. Then we analyzed the simulation using the ESP-r and Seoul weather data. In this research, the pattern of the increasing & decreasing rate of annual load according to the change of the various design factors is similar to the pattern of increasing & decreasing rate of not the K-values but the room temperature variation rate. Also we derive the optimum value of the various design factors and the room temperature variation rate in this analysis model. Further study is to be required the development of convenient tool to use in the real design.