• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.35-38
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• 2005

This paper suggests the method that forecast Dynamic Line Rating (DLR). Thermal Overload Risk (TOR) of next time is forecasted based on current weather condition and DLR value by Monte Carlo Simulation (MCS). To model weather element of transmission line for MCS, we will propose the use of weather forecast system and statistical models that time series law is applied. Also, through case study, forecasted TOR probability confirmed can utilize by standard that decide DLR of next time. In short, proposed method may be used usefully to keep safety of transmission line and reliability of supply of electric Power by forecasting transmission capacity of next time.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.225-232
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• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• The Plant Pathology Journal
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• v.21 no.2
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• pp.111-118
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• 2005

Weather data for disease forecasts are usually derived from automated weather stations (AWS) that may be dispersed across a region in an irregular pattern. We have developed an alternative method to simulate local scale, high-resolution weather and plant disease in a grid pattern. The system incorporates a simplified mesoscale boundary layer model, LAWSS, for estimating local conditions such as air temperature and relative humidity. It also integrates special models for estimating of surface wetness duration and disease forecasts, such as the grapevine downy mildew forecast model, DMCast. The system can recreate weather forecasts utilizing the NCEP/NCAR reanalysis database, which contains over 57 years of archived and corrected global upper air conditions. The highest horizontal resolution of 0.150 km was achieved by running 5-step nested child grids inside coarse mother grids. Over the Finger Lakes and Chautauqua Lake regions of New York State, the system simulated three growing seasons for estimating the risk of grape downy mildew with 1 km resolution. Outputs were represented as regional maps or as site-specific graphs. The highest resolutions were achieved over North America, but the system is functional for any global location. The system is expected to be a powerful tool for site selection and reanalysis of historical plant disease epidemics.

• The Journal of Bigdata
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• v.2 no.2
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• pp.129-140
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• 2017

Weather acts through low visibility, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity. Recently a variety of road weather big data sources such as CCTV, road sensor/systems, car sensor have been developed to solve the weather-related problems, This study identifies and defines the types and characteristics of these sources to suggest how to utilize them for car safety and efficiency as well as road management through analyzing domestic and oversea cases of road weather big data applications.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.201-205
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• 2016

• Journal of the Korean Society of Safety
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• v.10 no.1
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• pp.56-63
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• 1995

Two methods of the numerical method of CPQRA(Chemical Process Quantitative Risk Analysis) and the manual method of IAEA(International Atomic Energy Agency) were used to estimate the individual risk and societal risk around the chemical plant. Where, the CPQRA is introduced to verify the theoritical background of the manual of international atomic energy agency. The Gaussian plume model which has a weather stability class D with velocity of 5m/s was applied to calculate dispersion of hazard material. Also, 8-point method was employed to the effects of accidents for wind distribution. Furthermore, historical record, FTA(Fault Tree Analysis) and ETA(Event Tree Analysis) were used to estimate the probability or frequency of accidents. Eventually, the individual risk shows isorisk contour and the societal risk shows F-N curve around hazard facility, especially in chemical plants. Caulculated results, which both individual and societal risk, by using IAEA manual show simillar results to those of calculation by numerical method of CPQRA.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.257-258
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• 2015

The amount of damages caused by natural hazards is consistently growing due to the unusual weather and extreme events. At the same time, property damage by natural hazards is rapidly increasing as well. Hence, we need systematic anti-disaster activities and consulting that can react to such a situation. To address these needs, we investigated and analyzed insured claim payouts from natural hazards by administrative area, and calculate the risk index utilizing GIS. According to the index, this map is identifying the areas of greatest natural hazard risk. The ranking of natural disaster vulnerability based on the risk index, and risk grades were divided into five based on the ranking. This map integrates the natural hazard losses to assist in comprehensive and effective loss prevention activities using analysis of regional loss claims from natural hazards. Moreover, this map can be as utilized as loss mitigation and prevention activities to verify the distribution of exposure and hazards.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.142-151
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• 2017

Spontaneous intracerebral hemorrhage (sICH) has a high mortality rate. Research has demonstrated that sICH occurrence is related to weather conditions; therefore, this study used the decision tree method to explore the impact of climatic risk factors on sICH at different ages. The Taiwan National Health Insurance Research Database (NHIRD) and other open-access data were used in this study. The inclusion criterion was a first-attack sICH. The decision tree algorithm and random forest were implemented in R programming language. We defined a high risk of sICH as more than the average number of cases daily, and the younger, middle-aged and older groups were calculated as having 0.77, 2.26 and 2.60 cases per day, respectively. In total, 22,684 sICH cases were included in this study; 3,102 patients were younger (<44 years, younger group), 9,089 were middle-aged (45-64 years, middle group), and 10,457 were older (>65 years, older group). The risk of sICH in the younger group was not correlated with temperature, wind speed or humidity. The middle group had two decision nodes: a higher risk if the maximum temperature was >$19^{\circ}C$ (probability = 63.7%), and if the maximum temperature was <$19^{\circ}C$ in addition to a wind speed <2.788 (m/s) (probability = 60.9%). The older group had a higher risk if the average temperature was >$23.933^{\circ}C$ (probability = 60.7%). This study demonstrated that the sICH incidence in the younger patients was not significantly correlated with weather factors; that in the middle-aged sICH patients was highly-correlated with the apparent temperature; and that in the older sICH patients was highly-correlated with the mean ambient temperature. "Warm" cold ambient temperatures resulted in a higher risk of sICH, especially in the older patients.