• 수산경영론집
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• 제46권3호
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• pp.63-72
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• 2015

This study aims to empirically analyze the relationship between climate change elements and catch amount of coastal fisheries, which is predicted to be vulnerable to climate change since its business scale is too small and fishing ground is limited. Using panel data from 1974 to 2013 by region, we tested the relationship between the sea temperature, salinity and the coastal fisheries production. A spatial panel model was applied in order to reflect the spatial dependence of the ocean. The results indicated that while the upper(0-20m) sea temperature and salinity have no significant influence on the coastal fisheries production, the lower(30-50m) sea temperature has significant positive effects on it and, by extension, on the neighboring areas's production. Therefore, with sea temperature forecast data derived from climate change scenarios, it is expected that these results can be used to assess the future vulnerability to the climate change.

• 한국물환경학회지
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• 제36권6호
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• pp.568-580
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• 2020

Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.

• 한국직업건강간호학회지
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• 제17권1호
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• pp.55-63
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• 2008

Purpose: The purpose of this paper is to analyze the effect of job stress on work impairment. Method: 354 workers' data from Seoul and the Gyeonggi area were collected between February 1 and March 30 2006 by structured questionnaire. The questionnaire was meant to determine demographic data, job stress, and work impairment questionnaire. Data analyzed by SPSS 12.0 and AMOS 5.0 program. Results: Job stress was ranked job demand, insufficient job control, organizational system, lack of reward, job insecurity, interpersonal conflict, and occupational climate. The work impairment of completing work was increased when the stress of insufficient job control, lack of reward, job insecurity, and occupational climate were increasing. The work impairment of avoiding distraction was increased when the stress of job demand, insufficient job control, organizational system, lack of reward, job insecurity, and occupational climate were increasing. The stress of job demand, lack of reward, job insecurity, and occupational climate had an effect on avoiding distraction. The stress of lack of reward and occupational climate had an effect on completing work. Conclusion: If employers manage job stress of job demand, lack of reward, job insecurity, and occupational climate, their business will benefit.

• 한국농공학회논문집
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• 제52권6호
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• pp.27-37
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• 2010

The effect of potential future climate change on the storage rate of paddy field during storm periods (June - September) was assessed using the daily paddy water balance model. The CCCma CGCM2 data by SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (intergovernmental panel on climate change) was used to assess the future potential climate change. The future weather data for the year 2020s, 2050s and 2080s was downscaled by Change Factor method through bias-correction using 30 years weather data. The future (2020s, 2050s and 2080s) rainfall, storage and irrigation of paddy field, runoff in paddy levee and ponding depth were analyzed for the A2 and B2 climate change scenarios based on a base year (2005). The future irrigation change of paddy field was projected to increase by decrease in rainfall. So, runoff change in paddy levee was decrease slightly, future storage change of paddy was projected to increase.

• 임상간호연구
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• 제16권3호
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• pp.51-61
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• 2010

Purpose: The purpose of this study was to determine the effect of needs for professional development and organizational climate on organizational socialization of clinical nurses. A cross-sectional analysis were performed to assess the factors affecting organizational socialization. Methods: The data used in this study were obtained from clinical nurses who were employed in a hospital (N=606). Using multiple regression, we tested variables to assess their effects on organizational socialization in this sample. The data were analyzed using descriptive test, t-test, ANOVA, Pearson correlation coefficiency and stepwise multivariate regression. SPSS 17.0 program was utilized for data analysis. Results: The mean scores of organizational socialization, needs for professional development and organizational climate were statistically differed by career ladder, educational level and position. Organizational socialization had significant positive correlations with the needs for professional development (r=.332, p<.01) and organizational climate (r=.523, p<.01). Those variables including career ladder explained 33.4% of organizational socialization. Conclusion: Our findings indicate that organizational socialization of clinical nurses could be enhanced by meeting the needs for professional development and organizational climate. Developing innovative educations for encouraging clinical nurses' carrier development and creating a positive organizational climate are mandated for clinical nurses to have constructive organizational socialization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• 대한원격탐사학회지
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• 제33권5_3호
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• pp.773-786
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• 2017

본 연구는 텍스트마이닝 기법을 사용하여 사회기반시설에 대한 기후변화 영향 데이터들을 추출 및 분석하고, 이들을 행정구역 공간정보와 연계하여 분석 표출하고자 하였다. 우선 전력시설, 교통 도시기반시설, 유류 자원관리시설, 환경시설, 용수공급시설의 사회기반시설 중 다섯 가지(폭염, 한파, 호우, 대설, 강풍) 기후 요소로부터 영향을 많이 받은 시설을 파악하고, 각 시설별로 주요한 영향을 미치는 기후 요소를 분석하였다. 사회기반시설의 기후변화 영향은 시설의 위치에 영향을 받을 것으로 기대되어, 사회기반시설 기후변화 영향을 지역 중심으로 비교 분석 및 시각화 하였다. 연구 결과, 사회기반시설 중 교통 도시기반시설이 기후변화 영향을 가장 많이 받았으며, 사회기반시설에 대한 기후변화 영향은 주로 호우와 대설에 의해 발생하는 것으로 확인되었다. 사회기반시설 기후변화 영향의 공간정보를 분석 및 표출한 결과, 강원도와 서울 지역에 위치한 사회기반시설들이 기후변화 영향을 상대적으로 많이 받은 것으로 나타났다. 본 연구는 텍스트마이닝을 통해 사회기반시설 기후변화 영향에 대한 비정형화된 정보를 추출 및 처리하여 분석하고, 이를 공간정보로 표출 시도하였다는 점에서 의미가 있다.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.969-977
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• 2016

야외에서 재배되는 주요 채소류의 생산에 대한 기상변화의 영향력이 점차 커지고 있다. 기상변화로 인한 농작물 생산량의 변화는 공급과 수요의 불안정과 물가안정의 불안요소로 작용하고 있다. 본 논문에서는 패널회귀모형을 이용하여 기상상태에 따른 마늘의 생산량을 추정하였다. 2006년부터 2015년까지의 마늘 주산지 15곳의 10a당 마늘 생산량과 해당 지역의 기상자료를 사용하였다. 7가지 기상요인 (평균기온, 평균최저기온, 평균최고기온, 누적강수량, 누적일조시간, 평균상대습도, 평균지면온도)의 월별 (1월-12월)자료인 총 84개 기상변수중 다중회귀분석 단계선택방법을 통하여 7가지 기상변수를 선택하여 패널회귀모형에 사용하였다. 고정효과 모형과 확률효과 모형을 구분하는 하우스만 검정을 통하여 확률효과 모형으로 분석한 결과 평균최고기온 (1월), 누적강수량 (3월, 10월), 누적일조시간 (4월, 10월)등이 마늘 생산량 추정에 유의한 변수로 나타났다. 또한 연도별로 추정된 생산량 추정값의 추이가 실제 생산량과 동일한 추세를 보이고 있어 제안된 패널 회귀 모형이 잘 적합됨을 확인할 수 있다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• 대기
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• 제17권1호
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• pp.17-26
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• 2007

The station relocations, the replacement of instruments, and the change of a procedure for calculating derived climatic quantities from observations are well-known nonclimatic factors that seriously contaminate the worthwhile results in climate study. Prior to embarking on the climatological analysis, therefore, the quality and homogeneity of the utilized data sets should be properly evaluated with metadata. According to the metadata of the Korea Meteorological Administration (KMA), there have been plenty of changes in the procedure computing the daily mean values of temperature, humidity, etc, since 1904. For routine climatological work, it is customary to compute approximate daily mean values for individual days from values observed at fixed hours. In the KMA, fixed hours were totally 5 times changed: at four-hourly, four-hourly interval with additional 12 hour, eight-hourly, six-hourly, three-hourly intervals. In this paper, the homogeneity in the daily mean temperature dataset of the KMA was assessed with the consistency and efficiency of point estimators. We used the daily mean calculated from the 24 hourly readings as a potential true value. Approximate daily means computed from temperatures observed at different fixed hours have statistically different properties. So this inhomogeneity in KMA climate data should be kept in mind if you want to analysis secular aspects of Korea climate using this data set.