본 연구는 기계학습을 통한 수량예측모델을 이용하여 이상기상에 따른 WCM의 DMY 피해량을 산출하기 위한 목적으로 수행하였다. 수량예측모델은 WCM 데이터 및 기상 데이터를 수집 후 가공하여 8가지 기계학습을 통해 제작하였으며 실험지역은 경기도로 선정하였다. 수량예측모델은 기계학습 기법 중 정확성이 가장 높은 DeepCrossing (R2=0.5442, RMSE=0.1769) 기법을 통해 제작하였다. 피해량은 정상기상 및 이상기상의 DMY 예측값 간 차이로 산출하였다. 정상기상에서 WCM의 DMY 예측값은 지역에 따라 차이가 있으나 15,003~17,517 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 DMY 예측 값은 지역 및 각 이상기상 수준에 따라 차이가 있었으며 각각 14,947~17,571 kg/ha, 14,986~17,525 kg/ha 및 14,920~17,557 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 피해량은 각각 –68~89 kg/ha, -17~17 kg/ha 및 – 112~121 kg/ha 범위로 피해로 판단할 수 없는 수준이었다. WCM의 정확한 피해량을 산출하기 위해서는 수량예측모델에 이용하는 이상기상 데이터 수의 증가가 필요하다.

This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

The objective of this study was to select a model showing high-levels of interpretability which is high in R-squared value in terms of predicting the yield in the mixed pasture using the factors of fertilization, seeding rate and years after pasture establishment in steps, as well as the climate as a basic factor. The processes of constructing the yield prediction model for the mixed pasture were performed in the sequence of data collection (forage and climatic data), preparation, analysis, and model construction. Through this process, six models were constructed after considering climatic variables, fertilization management, seeding rates, and periods after pasture establishment years in steps, thereafter the optimum model was selected through considering the coincidence of the models to the forage production theories. As a result, Model VI (R squared = 53.8%) including climatic variables, fertilization amount, seeding rates, and periods after pasture establishment was considered as the optimum yield prediction model for mixed pastures in South Korea. The interpretability of independent variables in the model were decreased in the sequence of climatic variables(24.5%), fertilization amount(17.8%), seeding rates(10.7%), and periods after pasture establishment(0.8%). However, it is necessary to investigate the reasons of positive correlation between dry matter yield and days of summer depression (DSD) by considering cultivated locations and using other cumulative temperature related variables instead of DSD. Meanwhile the another research about the optimum levels of fertilization amounts and seeding rates is required using the quadratic term due to the certain value-centered distribution of these two variables

The objective of this study was to construct a forage rye (FR) dry matter yield (DMY) estimation model based on climate data by locations in South Korea. The data set (n = 549) during 29 years were used. Six optimal climatic variables were selected through stepwise multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the six climatic variables and cultivated locations as dummy variables was constructed as follows: DMY = 104.166SGD + 1.454AAT + 147.863MTJ + 59.183PAT150 4.693SRF + 45.106SRD 5230.001 + Location, where SGD was spring growing days, AAT was autumnal accumulated temperature, MTJ was mean temperature in January, PAT150 was period to accumulated temperature 150, SRF was spring rainfall, and SRD was spring rainfall days. The model constructed in this research could explain 24.4 % of the variations in DMY of FR. The homoscedasticity and the assumption that the mean of the residuals were equal to zero was satisfied. The goodness-of-fit of the model was proper based on most scatters of the predicted DMY values fell within the 95% confidence interval.

This study builds counties-specific panel data and establish a stochastic rice yield forecasting model by using a fixed effect panel model based on results calculating the coefficients for the meteorological factors, and by using a variety of weather scenarios. Rice yield prediction model developed estimating equations were set to rice yield as the dependent variable, and the average temperature, accumulated temperature, daily temperature range, sunshine hours as explanatory variables, by using panel data by counties in recent 10 years. Estimation results using a fixed-effects model was able to verify that an average temperature affects to yield as quadratic form, there appeared to be significantly affected by accumulated temperature in Heading period, an average temperature in Ripening period. a rice yield prediction model is meaningful in that we can see the forecasting results in the previous. not waiting the actual survey results provided by the National Statistical Office. because this forecasting estimates is sufficient rationale material by government supply & demand measures. Finally, the study leave to future challenges with respect to establishing a prediction model developed as combined with land productivity and environmental engineering factors.

Poisson model and Gamma-Poisson model are popularly used to analyze statistical behavior from defective data. The methods are based on binary criteria, that is, good or failure. However, manufacturing industries prefer polytomous criteria for classifying

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear

Yield is a very important measure that can expresses simply for productivity and performance of company. So, yield is used widely in many industries nowadays. With the development of the information technology and online based real-time process monitoring

최근 강우강도 및 패턴이 변화함에 따라 전세계적으로 토양유실이 증가하고 있다. 그 결과, 수생태계 건전성이 악화되고, 농업작물이 피해를 입어 수확량이 감소된다. 그동안 유출 및 토양유실을 예측하거나 비점오염원을 감소시키는 연구가 많이 수행되어왔다. USLE는 수년간 토양유실을 산정하는데 사용되어왔으나, 강우강도나 패턴변화를 적용하기에는 적절하지 못했다. 물리적 기반인 WEPP 모형은 다양한 강우강도 및 패턴변화를 적용하는데 적절하다. 본 연구에서는 WEPP 모형을 이용하여 Huff의 4분위, 다양한 강우간격, 설계강우에 따른 토양유실, 유출, 첨두유출을 산정하였다. 5분간격 강우 데이터와 60분 간격 강우 데이터를 비교한 결과 토양유실은 24%, 유출은 19%, 첨두유출은 16%가 차이나는 것으로 나타났다. 유출 및 토양유실이 5분 간격 강우량에서 실측치와 가장 유사한 것으로 나타나 강우데이터의 간격이 짧을수록 더 정확하게 모의할 수 있는 것으로 나타났다. Huff의 4분위를 이용하여 토양유실량, 유출량, 첨두유출량을 산정한 결과 토양유실량, 유출량, 첨두유출량 모두 3분위에서 가장 높게 발생하는 것으로 나타났다. 강원도 홍천지역 빈도별 확률강우를 이용하여 토양유실량, 유출량, 첨두유출량의 변화를 모의하였다. 2년 빈도와 300년 빈도에서 강우량은 167% 증가하였다. 유사량과 유출량, 첨두유출량은 각각 906.2%, 249.4%, 183.9% 증가하여 유사량의 증가율이 가장 큰 것으로 나타났다. 본 연구의 결과에서 보이는 바와 같이 WEPP 모형을 이용하여 향후 기후변화에 따른 유출 및 토양유실의 예측이 가능할 것으로 판단된다.

전세계적으로 토사유출은 심각한 문제로 알려져 있다. 환경관리자, 농부 및 다른 토지소유자들을 위해 다양한 모델링 테크닉이 개발되어왔고, 토양유실 저감을 위해 다양한 site-specific 최적관리기법의 효율을 산정하여 활용하였다. 물리적기반인 WEPP 모형은 시 공간적으로 작은 유역과 필지에서 발생하는 토양유실을 산정할 수 있다. 그러므로 본 연구에서는 WEPP watershed version을 이용하여 강원도 홍천군 자운리에 위치한 연구지역에 빗물

objective of the current study was to evaluate the change of rice yield under the projected climate change condition. The rice model included in "Decision Support System for Precise Management of Rice Culture” developed in Crop Environment and Production Technology Lab. of Seoul National University was validated prior to simulation experiment. For model input, the daily weather data were generated by SIMMETEO method from the monthly normal maximum and minimum temperatures and precipitation of the current period, 1971-2000 and the three periods in the future, 2011-2040, 2041-2070, and 2071-2100. The climate change projected using A1B emission scenario by Korea National Meteorological Institute was used for the periods in the future. Simulation experiments were carried out using three cultivars, Odaebyeo, Hwasungbyeo and Dongjinbyeo under six transplanting dates from May 10 to June 30. The vegetative and ripening period is expected to decrease respectively by 10 and 30 days in 2071-2100. High temperature-induced sterility is projected to increase by about 8% until 2071-2100. Rice yield on national average was simulated to decrease by 3, 7, and 13 % in 2011-2040. 2041-2070, and 2071-2100 periods, respectively. Though adaptation strategies that select the cultivar among the current cones and change the transplanting date would alleviate the yield decrease, the yield decrease of about 7% is still anticipated in 2071-2100.

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

A tank model in conjunction with Kalman filter is developed for prediction of sediment yield from an upland watershed in Northwestern Mississippi. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error. The sediment yield of each tank is computed by multiplying the total sediment yield by the sediment yield coefficient. The sediment concentration of the first tank is computed from its storage and the sediment concentration distribution (SCD); the sediment concentration of the next lower tank is obtained by its storage and the sediment infiltration of the upper tank; and so on. The sediment yield computed by the tank model using Kalman filter was in good agreement with the observed sediment yield and was more accurate than the sediment yield computed by the tank model.

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity (ECa) , slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with δi~leq2 were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and ECa were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.