• Journal of Environmental Impact Assessment
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• v.16 no.6
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• pp.407-419
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• 2007

This study investigated the predicted and abserved outflow of groundwater which occurred during tunnel constructions. Among the 586 road construction projects from 1986 to 2006, 4 route 25 tunnel construction areas and 26 waste water treatment facilities under construction were studied. Most of the tunnel outflow prediction in EIA (Environmental Impact Assessment) process have been classified into the 17 types of units depending on the assessor's options, which have not conformed to the request of the residents and non government organizations. The investigation results showed that the outflow of underground water in tunnel construction areas averaged about $0.133m^3/km{\cdot}min$ with the maximum $0.386m^3/km{\cdot}min$, and that the outflow mostly occurred in the early stage of tunnel excavation and diminished gradually. The prediction of outflow of underground water in the EIA process showed excessive results compared to observed outflow, the even 51.7 times. Consequently for more realistic prediction, current EIA method for prediction of outflow of underground water in tunnel construction areas has to adopt numerical methods coupled with hydraulics and geologic informations from unit methods of present time.

• Journal of Environmental Impact Assessment
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• v.9 no.1
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• pp.13-24
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• 2000

This paper presents a study of water quality model applications in environmental impact statements which were submitted during recent years in Korea. Most of the applications have reported that the development projects would have significant impacts on the water quality, especially, of streams and rivers. The water quality models, however, were hardly used as an impact prediction tool. Even in the cases where models were used, calibration and verification studies were not performed and thus the predicted results would not be reliable. These poor model applications in environmental impact assessment can be attributable to the fact that there were no available model application guidelines as well as no requirements by the review agency. In addition, the expected waste loads were improperly estimated in most cases, especially in non-point sources, and the predicted parameters were not good enough to understand water quality problems expected from the proposed plans. The effects of mitigation measures were not analyzed in most cases. Again, these can be attributed to no formal guidelines available for impact predictions until now. A brief guideline is described in this paper, including model selection, calibration and verification, impact prediction, and analysis of effects of mitigation measures. The results of this study indicate that the model application should be required to overcome the current improper predictions of environmental impacts and the guidelines should be developed in detail and provided.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Computers and Concrete
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• v.20 no.1
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• pp.49-56
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• 2017

A clear correlation exists between the compressive strength and elastic modulus of concrete. Unfortunately, determining the static elastic modulus requires destructive methods and determining the dynamic elastic modulus is greatly complicated by the shape and size of the specimens. This paper reports on a novel approach to the prediction of compressive strength in concrete cylinders using numerical calculations in conjunction with the impact-echo method. This non-destructive technique involves obtaining the speeds of P-waves and S-waves using correction factors through numerical calculation based on frequencies measured using the impact-echo method. This approach makes it possible to calculate the dynamic elastic modulus with relative ease, thereby enabling the prediction of compressive strength. Experiment results demonstrate the speed, convenience, and efficacy of the proposed method.

• Journal of Environmental Impact Assessment
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• v.19 no.1
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• pp.15-28
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• 2010

Because deterioration of air quality and urban heat island directly harm health of citizens, Health Impact Assessment (HIA) and Environmental Impact Assessment (EIA) for urban development projects needs to conduct analysis of their impacts objectively. This study aims to review appropriate methods for assessment of air quality used at each stage of urban development and to investigate prediction and assessment methods of urban heat island. In addition, by evaluating impacts of climate change following supposed urban construction performed in the central area of Korea on public health, it examines usefulness of HIA for urban construction. When urban heat island prediction and HIA method suggested in this study are applied to an imaginary city, they predict urban heat island properly and the impacts of climate changes on public health inside the city could be determined clearly by calculating life-climate index and bio-climate index related with thermal environment from the model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.631-635
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• 2006

In this study, simplified methods on measurement and evaluation of heavy-wight impact sound was proposed due to provide easy quality control method to construction engineers. The simplified methods include using of rubber impact ball instead of bang machine, reduced number of measuring and impact positions which is prescribed as over 4 points, using of hand-held sound level meter as a frequency analyser and prediction equation for $L_{i.Fmax.AW}$, single number rating, using $L_{Amax}$, and $L_{Lmax}$ at each frequency band. The results showed that a method of boundary driving and boundary measuring is the most similar to the current rating method.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• Journal of Environmental Impact Assessment
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• v.20 no.3
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• pp.397-409
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• 2011

This study was carried out for improving the effectiveness of water resources development project through local resident opinions in the environmental impact assessment(EIA). The EIA reports of seven dams were examined. Four dams -Youngju Dam, Seongduck Dam, Buhang Dam and Hantangang Dam- which included many local opinions including 470 opinions of 341 local residents were selected to be analyzed. Local residents submitted their opinions in the six fields which are meteorological phenomena, water quality, land use, fauna and flora, noise and vibration, and residence, and the major opinions of those opinions came from the atmosphere environment field which is 32% of total opinions and social and economic field which is 38% of total opinions, respectively. In submerged area, opinions of the measure for migration and compensation were 91% and in non-submerged area, opinions of the measure for meteorological phenomena was 86%. Those percentages were maximum in each area. Opinions concerned meteorological phenomena were 86% and 53% in Youngju Dam and Seongduck Dam where area is surrounded by existing dam, but there was only 9% and 0% of opinions in Buhang Dam and Hantangang Dam where area is without existing dam nearby. The reformation methods which reflected the resident's opinions were suggested on EIA in dam development projects. First of all, reliability and objectivity of the field of meteorological phenoma should be enhanced by scientific prediction of the phenomenon days. Secondly, techniques reducing uncertainty of various water quality prediction models ought to be developed and effectiveness of the reduction strategies in environmental impact should be quantified. Finally, the draft of EIA report should involve the detailed plans of migration and compensation's procedures, criteria and measures to support.