• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.619-622
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• 2015

Recently, there are growing fluctuations of productivity and price caused by severe weather conditions in the agriculture. Yield forecasting methods have been studied to solve the problems. This paper predicted yield per area, production area, and elements of weather based on the linear equation. A yield is calculated by multiplying the production area times the yield per area that is compensated using the weighted sum of the elements of weather. In experiments, proposed method shows that a forecasting precision is the more than 90%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.449-456
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• 2022

This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Network) based crop yield prediction model suitable for the domestic crop environment. The existing model has two characteristics. The first is that it replaces the original yield with the average yield of the year, and the second is that it trains the data of the predicted year. The new model uses the original field value to ensure accuracy, and the network structure has been improved so that it can train only with data prior to the year to be predicted. The proposed model predicted the yield per unit area of autumn cabbage for kimchi by region based on weather, soil, soil suitability classes, and yield data from 1980 to 2020. As a result of computing and predicting data for each of the four years from 2018 to 2021, the error amount for the test data set was about 10%, enabling accurate yield prediction, especially in regions with a large proportion of total yield. In addition, both the proposed model and the existing model show that the error gradually decreases as the number of years of training data increases, resulting in improved general-purpose performance as the number of training data increases.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.99-106
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• 2023

Recently, many studies on crop yield prediction using deep learning technology have been conducted. These algorithms have difficulty constructing a linear map between input data sets and crop prediction results. Furthermore, implementation of these algorithms positively depends on the rate of acquired attributes. Deep reinforcement learning can overcome these limitations. This paper analyzes the performance of DQN, Double DQN and Dueling DQN to improve crop yield prediction. The DQN algorithm retains the overestimation problem. Whereas, Double DQN declines the over-estimations and leads to getting better results. The proposed models achieves these by reducing the falsehood and increasing the prediction exactness.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.7
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• pp.469-480
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• 2017

For accurate AEP prediction in a wind farm, it is necessary to effectively calculate the wind speed reduction and the power loss due to the wake effect in each wind direction. In this study, a computer program for AEP prediction considering directional wake effect was developed. The results of the developed program were compared with the actual AEP of the wind farm and the calculation result of existing commercial software to confirm the accuracy of prediction. The applied equations are identical with those of commercial software based on existing theories, but there is a difference in the calculation process of the detection of the wake effect area in each wind direction. As a result, the developed program predicted to be less than 1% of difference to the actual capacity factor and showed more than 2% of better results compared with the existing commercial software.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.171-171
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• 2011

상수도의 합리적인 운용과 관리를 위해서는 급수량 예측이 매우 중요하다. 기존 급수량 예측은 신경망과 칼만 필터법을 사용한 연구들이 대부분이었다. 이러한 연구결과들은 높은 상관결과를 갖고 있지만 이는 자기상관계수에 대한 높은 의존도에 따른 결과로 볼 수 있다. 즉, 예측의 결과가 전날 수요량을 거의 그대로 따라오는 경향을 띄어, 급수량 예측 그래프가 기존 그래프를 오른쪽으로 이동시킨 것과 같이 나타난다. 본 연구에서는 이러한 문제점들을 해결하기 위해서 물수요량을 예측하는데 있어서 효과적인 예측인자를 도출하는 것이 우선되어야 할 것으로 판단되었다. 이에, 물수요량 특성을 효과적으로 나타내어 줄 수 있는 예측인자로서 강수량, 최저온도, 최고온도, 평균온도 등을 1차적으로 선정하였다. 이들 예측인자들과 서울시 물수요량과의 상관성을 평가하여 최적의 예측인자 Set과 지체시간 등을 산정하였다. 이렇게 선정된 예측인자와 Bayesian 통계기법 기반의 회귀분석 모형을 구축하여 물수요량을 예측하였다. 본 연구에서 적용하고자 하는 계층적 Bayesian 모형은 유사한 특성을 가지는 자료계열들 사이에서 서로 보완이 될 수 있는 정보들을 추출함으로써 모형이 갖는 불확실성을 상당히 줄일 수 있는 방법이다. 이러한 모형적 특징은 생산량 예측에 대한 불확실성 저감 측면에서 장점이 있을 것으로 판단된다. 본 연구에서는 광암, 암사, 구의, 뚝도, 영등포, 강북 정수장을 대상으로 모형의 적합성을 평가하였다. 이러한 연구결과는 향후 정수장 운영계획 및 동일한 시스템을 갖는 상수도 급수량 예측 시 유용하게 사용할 수 있을 것이다.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.307-320
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• 2000

It has been studied to find relationships between pine-mushroom yield and climatic factors. Recently, Hyun-Park, Key-I! shin and Hyun-Joong Kim(1998) investigated relationships between pine-mushroom yield and climatic factors by autoregression model. In this paper, to improve the forecast we suggest the conditional autoregression model using probability of existing pine-mushroom production.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.57-65
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• 2023

Various studies to enhance the accuracy of rice production forecasting are focused on improving the accuracy of the models. In contrast, there is a relative lack of research regarding the data itself, which the prediction models are applied to. When applying the same dependent variable and prediction model to two different sets of rice production data composed of distinct features, discrepancies in results can occur. It is challenging to determine which dataset yields superior results under such circumstances. To address this issue, by identifying potential influential features within the data before applying the prediction model and centering the modeling around these, it is possible to achieve stable prediction results regardless of the composition of the data. In this study, we propose a method to adjust the composition of the data's features in order to select optimal base variables, aiding in achieving stable and consistent predictions for rice production. This method makes use of the Korea Meteorological Administration's ASOS data. The findings of this study are expected to make a substantial contribution towards enhancing the utility of performance evaluations in future research endeavors.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.83-92
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• 2012

짐바브웨는 식량부족을 격어 오고 있으며, 이는 기후변화에 따른 수자원의 부족, 인구증가, 개발 및 환경보전 등으로 인하여 앞으로는 더욱 심화될 것으로 보인다. 3가지 배출시나리오 (A2, A1B, B1)에 대한 13개의 GCM 기후자료로부터 상세화한 기후예측값과 AquaCrop 작물모형을 이용하여 기후변화가 짐바브웨의 주곡인 옥수수의 수확량에 미치는 영향과 모형예측값의 불확실성을 분석하였다. 작물생육환경이 잘 유지된다고 가정하고 옥수수 잠재생산량을 모의한 결과 기준년도 (1970s)에 비해 2020s, 2050s and 2090s 년대에 평균 (범위) 8 % (6-9 %), 14 % (10-15 %) 및 16 % (11-17 %) 증가할 것으로 예측되었다. 같은 기간에 대한 물의 생산성은 평균 (범위) 7 % (4-13 %), 13 % (6-30 %) 및 15% (6-23 %) 증가할 것으로 예측되었다. 기온의 꾸준한 상승과 대기중 이산화탄소 농도 증가로 인한 시비효과로 인하여 미래에는 옥수수 단위 생산량과 물의 생산성이 증가할 것으로 예측되었으며 증가 범위를 보면 모형간의 변동성이 상당히 큰 것을 알 수 있었다. 본 연구결과는 기후변화가 짐바브웨의 옥수수 생산량에 미치는 영향과 변동성을 제시하므로서 장기적인 식량계획의 기초자료로 이용될 수 있을 것이다.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2014.10a
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• pp.1-24
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• 2014

This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis. Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world. We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively. The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change. Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.