• Atmosphere
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• v.33 no.4
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.1
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• pp.7-12
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• 2017

Most of existing buried pipes are composed of reinforced concrete. Reinforced concrete pipes have many problems such as aging, corrosion, leaking, etc. The polyethylene (PE) pipes have advantages to solve these problems. The plastic pipes buried underground are classified into a flexible pipe. National standard that has limited the long-term vertical deformation of the pipe to 5% for flexible pipes including PE pipe. This study presents a prediction for the long-term behavior of the polyethylene pipe based on ASTM D 5365. This prediction method is presented to estimate by using the statistical method from the initial deflection measurement data. We predict the behavior of long-term performance on the double-wall pipe and multi-wall pipe. As a result, it was found that the PE pipe will be sound enough more than 50 years if the compaction of soil around the pipe is more than 95% of the standard soil compaction density.

• Journal of the Korean Geotechnical Society
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• v.18 no.2
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• pp.107-121
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• 2002

In this study, GIS(Geographic Information System), a new approaching method, is proposed to effectively manage long-term settlements in the big reclamation sites. To verify an applicability of the proposed method, the prediction of long-term settlements which may occur in the overall soft deposits of the Incheon International Airport is carried out. During the process of the prediction of long-term settlements, measured settlement data obtained from an early stage of preloading are analyzed in detail. For purposes of the analysis, an estimation of the recompression index is also made based on the Nagaraj's research results. The coefficient of the secondary consolidation is further determined based on the relationship presented by the Mesri & Godlewski, which defines a ratio between the coefficient of the secondary consolidation and the recompression index.

• Journal of Environmental Impact Assessment
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• v.5 no.1
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• pp.95-105
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• 1996

Meteorological Joint Frequency Function required indispensably in long-term air quality prediction models were discussed for practical application in Korea. The algorithm, proposed by Turner(l964), is processed with daily solar insolation and cloudiness and height basically using Pasquill's atmospheric stability classification method. In spite of its necessity and applicability, the computer program, called STAR(STability ARray), had some significant difficulties caused from the difference in meteorological data format between that of original U.S. version and Korean's. To cope with the problems, revised STAR program for Korean users were composed of followings; applicability in any site of Korea with regard to local solar angle modification; feasibility with both of data which observed by two classes of weather service centers; and examination on output format associated with prediction models which should be used.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Journal of Power System Engineering
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• v.19 no.5
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• pp.45-51
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• 2015

Grade 91 steel is used for the major structural components of Generation-IV reactor systems such as a very high temperature reactor (VHTR) and sodium-cooled fast reactor (SFR). Since these structures are designed for up to 60 years at elevated temperatures, the prediction of long-term creep life is very important to determine an allowable design stress of elevated temperature structural component. In this study, a large body of creep rupture data was collected through world-wide literature surveys, and using these data, the long-term creep life was predicted in terms of three methods: Larson-Miller (L-M), Manson-Haferd (M-H) and Wilshire methods. The results for each method was compared using the standard deviation of error. The L-M method was overestimated in the longer time of a low stress. The Wilshire method was superior agreement in the long-term life prediction to the L-M and M-H methods.

• New & Renewable Energy
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• v.4 no.4
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.52.3-52.3
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• 2019

We investigate a relative contribution from short to long-term flaring rate to predicting M and X-class flare probabilities. In this study, we consider magnetic parameters summarizing distribution and non-potentiality by Solar Dynamics Observatory/Helioseimic and Magnetic Imager and flare list by Geostationary Operational Environmental Satellites. A short-term rate is the number of major flares that occurred in an given active region (AR) within one day before the prediction time. A mid-term rate is a mean flaring rate from the AR appearance day to one day before the prediction time. A long-term rate is a rate determined from a relationship between magnetic parameter values of ARs and their flaring rates from 2010 May to 2015 April. In our model, the predicted rate is given by the combination of weighted three rates satisfying that their sum of the weights is 1. We calculate Brier skill scores (BSSs) for investigating weights of three terms giving the best prediction performance using ARs from 2015 April to 2018 April. The BSS (0.22) of the model with only long-term is higher than that with only short-term or mid-term. When short or mid-term are considered additionally, the BSSs are improved. Our model has the best performance (BSS = 0.29) when all three terms are considered, and their relative contribution from short to long-term rate are 19%, 23%, and 58%, respectively. This model seems to be more effective when predicting active solar ARs having several major flares.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.

• Journal of the Korea Concrete Institute
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• v.18 no.4 s.94
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• pp.507-515
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• 2006

The purpose of the paper is to propose a method to give a more accurate prediction of prestress changes in prestressed concrete(PSC) bridges. The statistical approach of the method is using the measurement data of the structural system to develop a nonlinear regression analysis. Long-term prediction of prestress is achieved using nonlinear regression analysis. The proposed method is applied to the prediction of prestress of an actual prestressed concrete box girder bridge. The present study represents that confidence interval of long-term prediction becomes progressively narrower with the increase of in-situ measurement data. Therefore, the numerical results prove that a more realistic long-term prediction of prestress changes in PSC structures can be achieved by employing the proposed method. The prediction results can be efficiently used to evaluate prestress during the service life of structure so that the remaining prestress exceeds the control criteria.