• Journal of Ocean Engineering and Technology
• /
• v.37 no.5
• /
• pp.181-189
• /
• 2023

The fuel consumption of marine diesel engines holds paramount importance in contemporary maritime transportation and shapes energy efficiency strategies of ocean-going vessels. Nonetheless, a noticeable gap in knowledge prevails concerning the influence of ship hull conditions and propeller roughness on fuel consumption. This study bridges this gap by utilizing artificial intelligence techniques in Matlab, particularly convolutional neural networks (CNNs) to comprehensively investigate these factors. We propose a time-series prediction model that was built on numerical simulations and aimed at forecasting ship hull and propeller conditions. The model's accuracy was validated through a meticulous comparison of predictions with actual ship-hull and propeller conditions. Furthermore, we executed a comparative analysis juxtaposing predictive outcomes with navigational environmental factors encompassing wind speed, wave height, and ship loading conditions by the fuzzy clustering method. This research's significance lies in its pivotal role as a foundation for fostering a more intricate understanding of energy consumption within the realm of maritime transport.

• Journal of Korea Trade
• /
• v.25 no.4
• /
• pp.17-36
• /
• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.134-144
• /
• 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 Korean Society of Transportation
• /
• v.27 no.2
• /
• pp.133-144
• /
• 2009

In Korea, the ITS project has been progressed to improve traffic mobility and safety. Further, it is to relieve traffic jam by supply real time travel information for drivers and to promote traffic convenience and safety. It is important that the traffic information is provided accurately. This study was conducted outlier elimination and missing data adjustment to improve accuracy of raw data. A method for raise reliability of travel time prediction information was presented. We developed Historical Profile model and adjustment formula to reflect quality of interrupted flow. We predicted travel time by developed Historical Profile model and adjustment formula and verified by comparison between developed model and existing model such as Neural Network model and Kalman Filter model. The results of comparative analysis clarified that developed model and Karlman Filter model similarity predicted in general situation but developed model was more accurate than other models in incident situation.

• Journal of Korean Society for Atmospheric Environment
• /
• v.33 no.1
• /
• pp.11-18
• /
• 2017

The performance of a modeling system consisting of WRF model v3.3 and CMAQ model v4.7.1 for forecasting $PM_{2.5}$ concentrations were evaluated during the period May 2012 through December 2014. Twenty-four hour averages of $PM_{2.5}$ and its major components obtained through filter sampling at the Bulgwang intensive measurement station were used for comparison. The mean predicted $PM_{2.5}$ concentration over the entire period was 68% of the mean measured value. Predicted concentrations for major components were underestimated except for $NO_3{^-}$. The model performance for $PM_{2.5}$ generally tended to degrade with increasing the concentration level. However, the mean fractional bias (MFB) for high concentration above the $80^{th}$ percentile fell within the criteria, the level of accuracy acceptable for standard model applications. Among three bias correction methods, the ratio adjustment was generally most effective in improving the performance. Albeit for limited test conditions, this analysis demonstrated that the effects of bias correction were larger when using the data with a larger bias of predicted values from measurement values.

• Journal of Ocean Engineering and Technology
• /
• v.24 no.6
• /
• pp.7-15
• /
• 2010

Analyses of wind wave characteristics near the Korean marginal seas were performed in 2008 and 2009 by comparisons of an operational wind wave forecast model and ocean buoy data. In order to evaluate the model performance, its results were compared with the observed data from an ocean buoy. The model used in this study was very good at predicting the characteristics of wind waves near the Korean Peninsula, with correlation coefficients between the model and observations of over 0.8. The averaged Root Mean Square Error (RMSE) for 48 hrs of forecasting between the modeled and observed waves and storm surges/tide were 0.540 m and 0.609 m in 2008 and 2009, respectively. In the spatial and seasonal analysis of wind waves, long waves were found in July and September at the southern coast of Korea in 2008, while in 2009 long waves were found in the winter season at the eastern coast of Korea. Simulated significant wave heights showed evident variations caused by Typhoons in the summer season. When Typhoons Kalmaegi and Morakot in 2008 and 2009 approached to Korean Peninsula, the accuracy of the model predictions was good compared to the annual mean value.

• Journal of Environmental Science International
• /
• v.19 no.2
• /
• pp.171-184
• /
• 2010

Accurate simulation of the meteorological field is very important to assess the wind resources. Some researchers showed that sea surface temperature (SST) plays a leading role on the local meterological simulation. New Generation Sea Surface Temperature (NGSST), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Real-Time Global Sea Surface Temperature (RTG SST) have different spatial distribution near the coast and OSTIA shows the best accuracy compared with buoy data in the southeastern coast of the Korean Peninsula. Those SST products are used to initialize the Weather Research and Forecasting (WRF) Model for November 13-23 2008. The simulation of OSTIA shows better result in comparison with NGSST and RTG SST. NGSST shows a large difference with OSTIA in horizontal and vertical wind fields during the weak synoptic condition, but wind power density shows a large difference during strong synoptic condition. RTG SST shows the similar patterns but smaller the magnitude and the extent.

• Journal of Environmental Science International
• /
• v.12 no.9
• /
• pp.949-955
• /
• 2003

The subject basin of the research was the basin of Yeongcheon Dam located in the upper reaches of the Kumho River. The parameters of the model were derived from the results of abstracting topological properties out of rainfall-runoff observation data about heavy rains and Digital Elevation Modeling(DEM) materials. This research aimed at suggesting the applicability of the CELLMOD Model, a distribution-type model, in interpreting runoff based on the topological properties of a river basin, by carrying out runoff interpretation far heavy rains using the model. To examine the applicability of the model, the calculated peaking characteristics in the hydrograph was analyzed in comparison with observed values and interpretation results by the Clark Model. According to the result of analysis using the CELLMOD Model proposed in the present research for interpreting the rainfall-runoff process, the model reduced the physical uncertainty in the rainfall-runoff process, and consequently, generated improved results in forecasting river runoff. Therefore it was concluded that the algorithm is appropriate for interpreting rainfall-runoff in river basins. However, to enhance accuracy in interpreting rainfall-runoff it is necessary to supplement heavy rain patterns in subject basins and to subdivide a basin into minor basins for analysis. In addition, it is necessary to apply the model to basins that have sufficient observation data, and to identify the correlation between model parameters and the basin characteristics(channel characteristics).

• Structural Engineering and Mechanics
• /
• v.86 no.2
• /
• pp.181-195
• /
• 2023

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L-box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.

• Atmosphere
• /
• v.23 no.2
• /
• pp.143-160
• /
• 2013

The effect of 3DVAR (Three Dimension Variational data Assimilation) was examined by comparing observation and the simulations of CNTL (to which data assimilation was not applied) and ALL (to which data assimilation was applied using ground observation data and radar data) for the case of a heavy snowfall event (case A) of 11-12 February 2011 in the Yeongdong region. In case A, heavy snow intensively came in the Yeongdong coastal region rather than Daegwallyeong, in particular, around the Gangneung and Donghae regions with total precipitation in Bukgangneung at approximately 91 mm according to the AWS observation. It can be seen that compared to CNTL, ALL simulated larger precipitation along the Yeongdong coastline extending from Sokcho to Donghae while simulating smaller precipitation for inland areas including Daegwallyeong. On comparison of the total accumulated precipitations from simulations of CNTL and ALL, and the observed total accumulated precipitation, the positive effect of the assimilation of ground observation data and radar data could be identified in Bukgangneung and Donghae, on the other hand, the negative effect of the assimilation could be identified in the Daegwallyeong and Sokcho regions. In order to examine the average accuracy of precipitation prediction by CNTL and ALL for the entire Gangwon region including the major points mentioned earlier, the three hour accumulated precipitation from simulations of CNTL and ALL were divided into 5, 10, 15, 20, 25 and 30 mm/3hr and threat Scores were calculated by forecasting time. ALL showed relatively higher TSs than CNTL for all threshold values although there were some differences. That is, when considered generally based on the Gangwon region, the accuracy of precipitation prediction from ALL was improved somewhat compared to that from CNTL.