• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.667-680
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• 2021

We investigated changes in frost days and frost-free periods and to comparatively assess frost event prediction models developed using logistic regression (LR), random forest (RF), and long short-term memory (LSTM) networks. The meteorological variables for the model development were collected from the Suwon, Cheongju, and Gwangju stations for the period of 1973-2019 for spring (March - May) and fall (September - November). The developed models were then evaluated by Precision, Recall, and f-1 score and graphical evaluation methods such as AUC and reliability diagram. The results showed that significant decreases (significance level of 0.01) in the frequencies of frost days were at the three stations in both spring and fall. Overall, the evaluation metrics showed that the performance of RF was highest, while that of LSTM was lowest. Despite higher AUC values (above 0.9) were found at the three stations, reliability diagrams showed inconsistent reliability. A further study is suggested on the improvement of the predictability of both frost events and the first and last frost days by the frost event prediction models and reliability of the models. It would be beneficial to replicate this study at more stations in other regions.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.997-1007
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• 2018

Drought monitoring is the important system for disasters by climate change. To perform this, it is necessary to measure the precipitation based on satellite rainfall estimation. The data developed in this study provides two kinds of satellite data (raw satellite data and bias-corrected satellite data). The spatial resolution of satellite data is 10 km and the temporal resolution is 1 day. South Korea was selected as the target area, and the original satellite data was constructed, and the bias-correction method was validated. The raw satellite data was constructed using TRMM TMPA and GPM IMERG products. The GRA-IDW was selected for bias-correction method. The correlation coefficient of 0.775 between 1998 and 2017 is relatively high, and TRMM TMPA and GPM IMERG 10 km daily rainfall correlation coefficients are 0.776 and 0.753, respectively. The BIAS values were found to overestimate the raw satellite data over observed data. By using the technique developed in this study, it is possible to provide reliable drought monitoring to Korean peninsula watershed. It is also a basic data for overseas projects including the un-gaged regions. It is expected that reliable gridded data for end users of drought management.

• Journal of the Korean Professional Engineers Association
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• v.45 no.3
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• pp.28-35
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• 2012

IntPE-APEC Engineer and EMF-IRPE are the key role of Cross-Border Trade in Services at the Korea-U.S.A FTA. KPEA is the Representatives of ROK's registration for the IntPE(APEC Eng., EMF-IRPE). KPEA and IntPE(ROK) are actively promoting to get consulting/engineering services in the U.S.A within Korea-U.S.A FTA frame for the mutual benefit.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.349-355
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• 2009

한국-칠레 FTA를 시작으로 우리나라는 지역 통합 움직임으로 수출시장 확보라는 정책 목표를 세우고 동시 다발적인 FTA 추진하고 있다. 본 연구에서는 현재 국내 및 선진국의 FTA위 현황을 국가 자격증을 중심으로 분석하여 국내 자격 검정방안이 나아가야할 방향과 과제에 대하여 논하고자 한다. 현재 APEC을 중심으로 자격 상호인증 협정에 대한 심도 있는 연구가 진행 중이다. 따라서 본 연구에서는 APEC 상호인증을 중심으로 FTA 환경 하에서 국내 자격에 대한 상호인증 방안을 위한 문제점을 제시하고자 한다.

• The tire
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• s.170
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• pp.10-16
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• 1994

• TTA Journal
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• s.70
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• pp.113-153
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• 2000

• TTA Journal
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• s.53
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• pp.120-124
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• 1997

• the Consultant
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• s.21
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• pp.111-121
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• 2004

• the Consultant
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• s.21
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• pp.128-131
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• 2004