Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.
Abstract
Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.
참고문헌 (15)
- I. Chatterjee, "Development of a Machine Learning-Based Framework for Predicting Vessel Size Based on Container Capacity", Applied Science, 12(19), pp.1~18 (2022)
- I. Chatterjee, "Port Container Terminal Quay Crane Allocation, Based on Simulation and Machine Learning Method", Sensors and Materials, 34(2), pp.843~853 (2022)
- S. J. Ko, "A Study on Pipeline Design Methods for Providing Secure Container Image Registry", Journal of Internet of Things and Convergence, 9(3), pp.21~26 (2023)
- T. K. Kim, "IoT(Internet of Things)-based Smart Trash Can", Journal of Internet of Things and Convergence, 6(1), pp.17~22 (2020)
- T. K. Kim, "Self-powered Wireless Bus Information and Disaster Information System based on Internet of Things(IoT)", Journal of Internet of Things and Convergence, 8(1), pp.17~22 (2022)
- D. H. Kim, "Forecasting the Container Volumes of Busan Port using LSTM", Journal of Korea Port Economic Association, 36(2), pp.53~62 (2020)
- G. D. Yi, "Forecasting the Container throughput of the Busan Port using a Seasonal Multiplicative ARIMA Model", Journal of Korea Port Economic Association, 29(3), pp.1~23 (2013)
- E. J. Lee, "Container Volume Prediction using Time-Series Decomposition with a Long Short-Term Memory Models", Applied Sciences, 11(19), pp.8995~ (2021)
- S. Filom, "Applications of Machine Learning Methods in Port Operations–A Systematic Literature Review", Transportation Research Part E : Logistics and Transportation Review, 161, pp.102722~ (2022)
- D. Tarwidi, "An Optimized XGBoost-based Machine Learning Method for Predicting Wave Run-up on a Sloping Beach", MethodsX, 10, pp.102119~ (2023)
- T. Chen, "Xgboost : A Scalable Tree Boosting System", Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp.785~794 (2016)
- G. S. Cho, "Application of Shared Logistics Operation Platform of ICT-based Logistics Companies", Journal of Internet of Things and Convergence, 8(5), pp.27~31 (2022)
- C. S. Bojer, "Kaggle Forecasting Competitions : An Overlooked Learning Opportunity", International Journal of Forecasting, 37(2), pp.587~603 (2021)
- A. D. Myttenaere, "Mean Absolute Percentage Error for Regression Models", Neurocomputing, 192, pp.38~48 (2016)
- N. G. Divergences, "WORLD ECONOMIC OUTLOOK", World Economic Outlook, ~ (2023)