• 한국환경과학회지
• /
• 제20권3호
• /
• pp.395-404
• /
• 2011

Weather forecasting is one of the key elements to improve health through the prevention and mitigation of health problems. Health forecasting is a potential resource creating enormous added value as it is effectively used for people. The purpose of this study is to estimate 'Willingness to Pay' for health forecasting. This survey was carried out to derive willingness to pay from 400 people who lived in Busan and Kyungnam Province and over 30 years of age during the period of July 1-31, 2009. The results showed that a 47.50% of people had intention to willingness to pay for health forecasting, and the pay was 7,184.21 won per year. Willing to pay goes higher depending on 'tax burden as to benefit of weather forecasting', 'importance of the weather forecasting in the aspect of health', 'satisfaction to the weather forecasting', and 'frequency of health weather index check'. This study followed the suggestion of the Korea Meteorological Administration generally and the values derived through surveys could be reliable. It can be concluded that a number of citizens who are willing to pay for health forecasting are high enough to meet the costs needed to provide health forecasting.

• Water Engineering Research
• /
• 제3권2호
• /
• pp.113-122
• /
• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• 한국수자원학회논문집
• /
• 제44권10호
• /
• pp.843-851
• /
• 2011

수문학 분야에서 중장기 유출량 예측은 입력변수의 불확실성 등으로 인하여 확률론적 방법을 사용하는 것이 바람직한 것으로 알려져 왔다. 본 연구에서는 금강유역을 대상으로 구성된 바 있는 RRFS-ESP 시스템에 PDF-ratio 방법을 기반으로한 사전처리기능을 장착하여 보다 효율적인 중장기 예측시스템으로의 확장을 시도하여 보았다. 이를 위하여 기상청에서 제공하는 확률기상정보를 이용하여 가중치를 산정하고 이를 기반으로 시나리오별 예측확률을 갱신하였다. 예측결과에 대하여 각 기법별 예측점수를 산정하여 본 결과 우선 ESP 기법에 의한 예측점수의 평균이 초보예측 점수를 상회하여 본 연구에서 구성한 RRFS-ESP 시스템의 적용성을 확인할 수 있었다. 또한 확률기상전망을 이용하여 갱신한 유입량 시나리오의 예측점수가 ESP 기법에 의한 예측점수를 상회하고 있음을 확인할 수 있어 ESP 기법에 의한 예측결과를 확률기상전망을 이용하여 갱신할 경우 예측 정확도를 보다 개선시킬 수 있음을 확인할 수 있었다.

• Water Engineering Research
• /
• 제3권2호
• /
• pp.123-134
• /
• 2002

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

• 설비공학논문집
• /
• 제25권6호
• /
• pp.310-316
• /
• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• International Journal of Industrial Entomology and Biomaterials
• /
• 제25권2호
• /
• pp.163-169
• /
• 2012

A decrease in pass ratio of Korean exporting pears causes several negative effects including an increase in pesticide dependency. In this study, we attempted to establish the pest forecasting management system, composed of weekly field forecasting by pear farmers, meteorological data obtained by automatic weather station (AWS), newly designed internet web page ($\underline{http://pearpest.jnu.ac.kr/}$) as information collecting and providing ground, and information providing service. The weekly field forecasting information on major pear diseases and pests was collected from the forecasting team composed of five team leaders from each pear exporting complex. Further, an abridged weather information for the prediction of an infestation of major disease (pear scab) and pest (pear psylla and scale species) was obtained from an AWS installed at Bonghwang in Naju City. Such information was then promptly uploaded on the web page and also publicized to the pear famers specializing in export. We hope this pest forecasting management system increases the pass ratio of Korean exporting pears throughout establishment of famer-oriented forecasting, inspiring famers' effort for the prevention and forecasting of diseases and pests occurring at pear orchards.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2015년도 추계학술대회
• /
• pp.619-622
• /
• 2015

최근 심각한 기후변화로 인하여 농산물 생산성 및 농산물 가격의 변동성이 커지고 있으며, 이러한 문제를 해결하기 위하여 농산물의 생산량을 예측하는 방법이 연구되고 있다. 본 논문에서는 선형 방정식을 이용하여 생산 단수, 생산 면적, 기후 요소를 예측하였으며, 기후 요소의 가중치 합을 이용하여 보정된 생산 단수와 생산 면적을 곱하여 생산량을 계산하였다. 실험에서 예측한 생산량은 실제 생산량과 비교하여 약 90% 이상의 예측율을 보였다.

• 한국수자원학회논문집
• /
• 제37권6호
• /
• pp.449-460
• /
• 2004

본 논문에서는 월 댐유입량을 예측하는데 있어서 기상예보정보를 활용한 뉴로-퍼지 시스템의 적용성을 검토하였다. 뉴로-퍼지 알고리즘으로 퍼지이론과 신경망이론의 결합형태인 ANFIS(Adaptive Neuro-Fuzzy Inference System)을 이용하여 모형을 구성하였다. ANFIS의 공간분할에 의한 제어규칙의 선정에 있어 퍼지변수가 증가함에 따라 제어규칙이 기하급수적으로 증가하는 단점을 해결하기 위해 퍼지 클러스터링(Fuzzy Clustering)방법 중 하나인 차감 클러스터링(Subtractive Clustering)을 사용하였다. 또한 본 연구에서는 정성적인 기상예보정보를 정량화 시키는 방법을 제안하였다. AMFIS를 이용하여 월 댐유입량 예측 시, 관측자료만으로 구성된 모형에 의한 예측결과와 관측자료에 기상예보정보를 더하여 구성된 모형에 의한 예측결과를 비교하였다. 그 결과 ANFIS는 기상예보정보를 활용하여 댐유입량을 예측했을 때가 관측자료만으로 예측했을 때보다 예측능력이 더욱 정확함을 보였다.

• The Plant Pathology Journal
• /
• 제26권1호
• /
• pp.37-48
• /
• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• 한국태양에너지학회 논문집
• /
• 제26권2호
• /
• pp.27-34
• /
• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.