Prediction Oil and Gas Throughput Using Deep Learning
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (13)
- Korea Port and Shipping Report, "Korea Maritime Institute", ~ (2021)
- Dejan Dragan, "Throughput forecasting of different types of cargo in the adriatic seaport Koper", Maritime Policy & Management, 48(1), pp.19~45 (2021), Informa UK Limited[DOI]
- UNCTAD, "Review of Maritime Transport", ~ (2021)
- Alen Jugović, "Traffic Demand Forecasting for Port Services", Promet-Traffic & Transportation, 23(1), pp.59~69 (2012), Faculty of Transport and Traffic Sciences[DOI]
- Peter W. De Langen, "Combining Models and Commodity Chain Research for Making Long-Term Projections of Port Throughput: an Application to the HamburgLe Havre Range", European Journal of Transport and Infrastructure Research, 12(3) (2012), TU Delft OPEN Publishing[DOI]
- Yasmine Rashed, "Short-term forecast of container throughout: An ARIMA-intervention model for the port of Antwerp", Maritime Economics & Logistics, 19(4), pp.749~764 (2017), Springer Science and Business Media LLC[DOI]
- K. Mokhtar, "Design in Maritime Engineering. Advanced Structured Materials" (2022), Springer
- Hing Kai Chan, "A comparison of time series methods for forecasting container throughput", International Journal of Logistics Research and Applications, 22(3), pp.294~303 (2018), Informa UK Limited[DOI]
- 김정은, "유류화물 항만물동량 예측모형 개발 연구", 한국항만경제학회지, 34(1), pp.81~98 (2018), 한국항만경제학회[DOI]
- Javed Farhan, "Forecasting seasonal container throughput at international ports using SARIMA models", Maritime Economics and Logistics, 20(1), pp.131~148 (2018), Springer Science and Business Media LLC[DOI]
- Penn Collins Awah, "Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala", Journal of Marine Science and Engineering, 9(7), pp.720~ (2021), MDPI AG[DOI]
- Sonali Shankar, "Forecasting container throughput with long short-term memory networks", Industrial Management & Data Systems, 120(3), pp.425~441 (2019), Emerald[DOI]
- Nguyen Duy Tan, "Time series forecasting for port throughput using recurrent neural network algorithm", Journal of International Maritime Safety, Environmental Affairs, and Shipping, 5(4), pp.175~183 (2021), Informa UK Limited[DOI]