• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.617-624
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• 2017

The production of cabbage, which is mainly cultivated in open field, varies greatly depending on weather conditions, and the price fluctuation is largely due to the presence of a substitute crop. Previous studies predicted the production of cabbage using actual weather data, but in this study, we predicted the wholesale price using unstructured agricultural meteorological data on the web. From January 2009 to October 2016, we collected documents including the cabbage on the portal site, and extracted keywords related to weather in the collected documents. We compared the forecast wholesale prices of simple models and unstructured agricultural weather models at the time of shipment. The simple model is AR model using only wholesale price, and the unstructured agricultural weather model is AR model using unstructured agricultural weather data additionally. As a result, the performance of unstructured agricultural weather model was has been found to be more accurate prediction ability.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.324-324
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• 2021

2020년 우리나라 중부지역의 장마철('20.6.10.~8.16.) 기간이 54일로 1973년 전국적인 기상관측을 시작한 이래 가장 길었고, 장마철 전국강수량(686.9mm)은 역대 2위로(1위 '06년 699.1mm) 약 8,000여명의 수재민과 42명의 인명피해가 발생하였다. 이처럼 '이상기후'라 부르는 극한 기상 사건들인 홍수, 폭우, 폭염, 가뭄 등의 자연재해가 해마다 우리나라 포함 전 세계적으로 자주 발생하면서 수문기상 정보 관리 및 활용의 중요성 또한 점차 커지고 있다. 이를 위해 기상청에서는 일반국민과 물관리 관계기관(회원)의 편의와 자료의 활용 증진을 위해 유역별 수문기상 관측·예측 정보를 생산하여 서비스를 제공하고 있다(https://hydro.kma.go.kr). 수문기상 관측 정보는 유역별 면적강수량, 증발산량 등을 산출하여 GIS기반으로 실시간 자료와 2000년 이후 과거 관측강수량의 기간(월, 계절, 연)자료를 제공하며, 예측 자료는 초단기 수치 모델(KLAPS), 레이더(MAPLE), 수문기상 예측모델(UM3km), 한국형 수치 예보 모델(KIM)을 활용하고 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.412-414
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• 2017

It is important to predict price of agricultural products accurately to government, local government, bodies in charge of agriculture. Production and shipping of agricultural products are affected by weather condition significantly. In this research, prediction model of a Chinese cabbage which is highly sensitive to weather condition is proposed using deep learning technique. After performance of proposed model and a model of previous research is compared, superiority of proposed model is proved.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.219-224
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• 2022

The Korea Meteorological Administration(KMA) has been released life-related indexes such as 'Life industrial weather information' and 'Safety weather information' while other countries' meteorological administrations have been made 'Human-biometeorology' and 'Health meteorology' indexes that concern human sensibility effections to diverse criteria. Although human sensibility changes have been studied in psychological research criteria with diverse and innumerous application areas, there are not enough studies that make data mining based validation of sensibility change factors. In this research I made models to estimate sensibility change caused by weather factors such as temperature and humidity, and validated by collecting sensibility data from SNS text crawling and weather data from KMA public dataset. By Logistic Regression, I clarify factors affecting sensibility changes.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.113-116
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• 2001

90년대 이후 다양한 첨단전산기술들이 작물모형분야에도 이용되기 시작하였는데 농업생태계 평가를 위한 국지단위 모의모형의 활용, 인공위성 등을 이용한 원격계측정보의 활용, GIS기술 등은 농업분야에서 널리 활용되고 있는 첨단 신기술의 대표적인 사례가 되고 있다. 이러한 신기술들은 농업기상정보를 생산하는 데에 이용될 뿐만 아니라 가장 중요한 환경요소로서 농업기상정보를 필요로 한다(Munakata, 1976).(중략)

• Journal of Korea Water Resources Association
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• v.37 no.6
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• pp.449-460
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• 2004

The purpose of this study is to test the applicability of neuro-fuzzy system for monthly dam inflow forecasts by using weather forecasting information. The neuro-fuzzy algorithm adopted in this study is the ANFIS(Adaptive neuro-fuzzy Inference System) in which neural network theory is combined with fuzzy theory. The ANFIS model can experience the difficulties in selection of a control rule by a space partition because the number of control value increases rapidly as the number of fuzzy variable increases. In an effort to overcome this drawback, this study used the subtractive clustering which is one of fuzzy clustering methods. Also, this study proposed a method for converting qualitative weather forecasting information to quantitative one. ANFIS for monthly dam inflow forecasts was tested in cases of with or without weather forecasting information. It can be seen that the model performances obtained from the use of past observed data and future weather forecasting information are much better than those from past observed data only.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.309-309
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• 2019

기후예측모델을 통해 일단위 강수의 예측정보가 제공되고 있지만, 실제 강수량자료와 시공간적 편의로 인해 수문학적 활용은 한계가 있다. 일반적으로 기후모델의 시공간적 해석 규모 및 예측정확성을 고려할 때 계절단위에서 예측정보의 활용이 가장 현실적인 것으로 알려지고 있다. 그러나 수문해석 시 시공간적 해상도가 낮아 직접적인 활용은 어려운 상황이며, 수문해석 모형의 입력자료로 활용 시 편의보정 및 상세화 과정이 일반적으로 요구된다. 본 연구에서는 기후모델로부터 얻은 강우예측결과에 Bayesian 모델 기반의 편의보정-상세화 기법을 개발하여 강우예측정보의 활용성을 개선하고자 한다. 이 과정에서 Bayesian Copula 모델을 이용한 이변량 형태의 예측강수의 검보정 방법을 개발하였으며, 특히 기후모델 이외의 기상 상태변량인 해수면온도(sea surface temperature, SST)를 예측인자로 추가하여 Hybrid 형태의 계절 앙상블 강우예측모델을 개발하고자 한다.