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@article{ART002960862},
author={Sangseop Lim},
title={Prediction Oil and Gas Throughput Using Deep Learning},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={5},
pages={155-161},
doi={10.9708/jksci.2023.28.05.155}

TY - JOUR
AU - Sangseop Lim
TI - Prediction Oil and Gas Throughput Using Deep Learning
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 5
PB - The Korean Society Of Computer And Information
SP - 155
EP - 161
SN - 1598-849X
AB - 97.5% of our country's exports and 87.2% of imports are transported by sea, making ports an important component of the Korean economy. To efficiently operate these ports, it is necessary to improve the short-term prediction of port water volume through scientific research methods. Previous research has mainly focused on long-term prediction for large-scale infrastructure investment and has largely concentrated on container port water volume. In this study, short-term predictions for petroleum and liquefied gas cargo water volume were performed for Ulsan Port, one of the representative petroleum ports in Korea, and the prediction performance was confirmed using the deep learning model LSTM (Long Short Term Memory). The results of this study are expected to provide evidence for improving the efficiency of port operations by increasing the accuracy of demand predictions for petroleum and liquefied gas cargo water volume. Additionally, the possibility of using LSTM for predicting not only container port water volume but also petroleum and liquefied gas cargo water volume was confirmed, and it is expected to be applicable to future generalized studies through further research.
KW - Short-term forecasting;LSTM;deep learning;oil and gas throughput;port efficiency
DO - 10.9708/jksci.2023.28.05.155
ER -

Sangseop Lim. (2023). Prediction Oil and Gas Throughput Using Deep Learning. Journal of The Korea Society of Computer and Information, 28(5), 155-161.

Sangseop Lim. 2023, "Prediction Oil and Gas Throughput Using Deep Learning", Journal of The Korea Society of Computer and Information, vol.28, no.5 pp.155-161. Available from: doi:10.9708/jksci.2023.28.05.155

Sangseop Lim "Prediction Oil and Gas Throughput Using Deep Learning" Journal of The Korea Society of Computer and Information 28.5 pp.155-161 (2023) : 155.

Sangseop Lim. Prediction Oil and Gas Throughput Using Deep Learning. 2023; 28(5), 155-161. Available from: doi:10.9708/jksci.2023.28.05.155

Sangseop Lim. "Prediction Oil and Gas Throughput Using Deep Learning" Journal of The Korea Society of Computer and Information 28, no.5 (2023) : 155-161.doi: 10.9708/jksci.2023.28.05.155

Sangseop Lim. Prediction Oil and Gas Throughput Using Deep Learning. Journal of The Korea Society of Computer and Information, 28(5), 155-161. doi: 10.9708/jksci.2023.28.05.155

Sangseop Lim. Prediction Oil and Gas Throughput Using Deep Learning. Journal of The Korea Society of Computer and Information. 2023; 28(5) 155-161. doi: 10.9708/jksci.2023.28.05.155

Sangseop Lim. Prediction Oil and Gas Throughput Using Deep Learning. 2023; 28(5), 155-161. Available from: doi:10.9708/jksci.2023.28.05.155

Sangseop Lim. "Prediction Oil and Gas Throughput Using Deep Learning" Journal of The Korea Society of Computer and Information 28, no.5 (2023) : 155-161.doi: 10.9708/jksci.2023.28.05.155