• The Journal of Fisheries Business Administration
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• v.46 no.3
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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.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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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.

• Korean Journal of Occupational Health Nursing
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• v.17 no.1
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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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.

• Journal of Korean Clinical Nursing Research
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• v.16 no.3
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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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• v.16 no.5
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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.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.773-786
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• 2017

This study was to analyze data of climate change impacts on social infrastructure using text-mining methodology, and to visualize the spatial information by integrating those with regional data layers. First of all, the study identified that the following social infrastructure; power, oil and resource management, transport and urban, environment, and water supply infrastructures, were affected by five kinds of climate factors (heat wave, cold wave, heavy rain, heavy snow, strong wind). Climate change impacts on social infrastructure were then analyzed and visualized by regions. The analysis resulted that transport and urban infrastructures among all kinds of infrastructure were highly impacted by climate change, and the most severe factors of the climate impacts on social infrastructure were heavy rain and heavy snow. In addition, it found out that social infrastructure located in Seoul and Gangwon-do region were relatively largely affected by climate change. This study has significance that atypical data in media was used to analyze climate change impacts on social infrastructure and the results were translated into spatial information data to analyze and visualize the climate change impacts by regions.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.969-977
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• 2016

Climate change affects the growth of crops which were planted especially in fields, and it becomes more important to use climate data to predict the yields of the major vagetables. The variation of the crop products caused by climate change is one of the significant factors for the discrepancy of the demand and supply, and leads to the price instability. In this paper, using a panel regression model, we predicted the garlic yields with the weather conditions of different regions. More specifically we used the panel data of the several climate variables for 15 main garlic production areas from 2006 to 2015. Seven variables (average temperature, average maximum temperature, average minimum temperature, average surface temperature, cumulative precipitation, average relative humidity, cumulative duration time of sunshine) for each month were considered, and most significant 7 variables were selected from the total 84 variables by the stepwise regression. The random effects model was chosen by the Hausman test. The average maximum temperature (January), the cumulative precipitation (March, October), the cumulative duration time of sunshine (April, October) were chosen among the variables as the significant climate variables of the model

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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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.

• Atmosphere
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• v.17 no.1
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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.