• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.4
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• pp.445-451
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• 2012

We developed an age-based spawner-recruit model incorporating environmental variables to forecast stock biomass and recruits of pelagic fish in this study. We applied the model to the Tsushima stock of jack mackerel, which is shared by Korea and Japan. The stock biomass of jack mackerel (Trachurus japonicus) around Korean waters ranged from 141 thousand metric tons (mt) and 728 thousand mt and recruits ranged from 27 thousand mt to 283 thousand mt. We hind-casted the stock biomass to evaluate the model performance and robustness for the period of 1987~2009. It was found that the model has been useful to forecast stock biomass and recruits for the period of the lifespan of fish species. The model is also capable of forecasting the long-term period, assuming a certain climatic regime.

• Environmental Engineering Research
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• v.22 no.2
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.1-12
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• 2021

Altman's Z-score is used to measure a company's financial health and to predict the probability that a company will collapse within 2 years. It is proven to be very accurate to forecast bankruptcy in a wide variety of contexts and markets. The goal of this study is to use Altman's Z-score model to forecast insolvency in non-financial publicly traded enterprises. Non-financial firms are a significant industry in Malaysia, and current trends of consolidation and long-term government subsidies make assessing the financial health of such businesses critical not just for the owners, but also for other stakeholders. The sample of this study includes 84 listed companies in the Kuala Lumpur Stock Exchange. Of the 84 companies, 52 are considered high risk, and 32 are considered low-risk companies. Secondary data for the analysis was gathered from chosen companies' financial reports. The findings of this study show that the Altman model may be used to forecast a company's financial collapse. It dispelled any reservations about the model's legitimacy and the utility of applying it to predict the likelihood of bankruptcy in a company. The findings of this study have significant consequences for investors, creditors, and corporate management. Portfolio managers may make better selections by not investing in companies that have proved to be in danger of failing if they understand the variables that contribute to corporate distress.

• Proceedings of the Materials Research Society of Korea Conference
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• 2008.11a
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• pp.4.1-4.1
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• 2008

• Fashion Information and Technology
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• v.4
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• pp.25-33
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• 2007

• Atmosphere
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• v.16 no.2
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• pp.141-157
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• 2006

A revolution in weather and climate forecasting is in progress. This has been made possible as a result of theoretical advances in our understanding of the predictability of weather and climate, and by the extraordinary developments in supercomputer technology. New problem areas have been discovered and different solutions have been found by the recent high performance computers whose performance has been increased rapidly. Such advances in the computational performance may change the strategy of development of numerical models and prediction methods. This paper discusses a brief history and current status of the supercomputers in institutions for research and forecast of weather/climate. The main purpose of this study is to provide the preliminary information about supercomputers such as architecture of system and processor. Such information would be useful for meteorologists to understand the features and the preference of supercomputers in each institution.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.10-19
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• 2014

The purpose of the present study is to examine the satisfaction level towards residential environment forecast on directions in geomantic studies, or Feng Shui and Ming Lihak. For the subjects of this study, 100 ordinary people aged from teens to sixties were chosen for research from August through December in 2012. Frequency analysis and Krushkal Wallis test were used for the analysis, and all empirical analyses were performed at 5% significance level. Analysis results show that satisfaction level towards directions in the residential environment is high where individual preferences are adjusted to form harmony with the force and balance of the directions. And, in a residential environment, the highest level of satisfaction was marked with directions of wood pillows, or Duchim, signifying improved efficacy of forecast of residential environment change. Findings of the study suggest that high satisfaction level in the directions of Feng Shui and the Ming Lihak forecasts high quality life in the given residential structure and structural forms. bstract.

• Journal of Wetlands Research
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• v.19 no.3
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• pp.366-374
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• 2017

The objective of this study is to explore the applicability of very short-range-forecast rainfall for the early warning of mud-debris flows. An artificial neural network was applied to use the very short-range-forecast rainfall data. The neural network is learned by using the relationship between the radar and the AWS, and forecasted rainfall is estimated by replacing the radar rainfall with the MAPLE data as the very short-range-forecast rainfall data. The applicability of forecasted rainfall by the MAPLE was compared with the AWS rainfall at the test-bed using the rainfall criteria for cumulative rainfall of 6hr, 12hr, and 24hr respectively. As a result, it was confirmed that forecasted rainfall using the MAPLE can be issued prior to the AWS warning.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• Atmosphere
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• v.13 no.4
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• pp.71-79
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• 2003

We present a pre processing system for the Atmospheric Infrared Sounder (AIRS) sounding suite onboard Aqua satellite. With its unprecedented 2378 channels in IR bands, AIRS aims at achieving the sounding accuracy [s1]of a radiosonde (1 K in 1-km layer for temperature and 10% in 2-km layer for humidity). The core of the pre p rocessor is the International MODIS/AIRS Processing Package (IMAPP) that performs the geometric and radiometric correction to compute the Earth's radiance. Then we remove spurious data and retrieve the brightness temperature (Tb). Since we process the direct-broadcast data almost for the first time among the AIRS directbroadcast community, special attention is needed to understand and verify the products. This includes the pixel-to-pixel verification of the direct-broadcast product with reference to the fullorbit product, which shows the difference of less than $10^{-3}$ K in IR Tb.