@article{ART003054124},
author={Nguyen Thi Phuong Thanh and Cho Gyu Sung},
title={Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2024},
volume={10},
number={1},
pages={39-45},
doi={10.20465/KIOTS.2024.10.1.039}
TY - JOUR
AU - Nguyen Thi Phuong Thanh
AU - Cho Gyu Sung
TI - Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model
JO - Journal of Internet of Things and Convergence
PY - 2024
VL - 10
IS - 1
PB - The Korea Internet of Things Society
SP - 39
EP - 45
SN - 2466-0078
AB - 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.
KW - Internet of Things (IoT);container throughput forecasting;cargo volume;machine learning (ML);XGBoost technique;Busan Port
DO - 10.20465/KIOTS.2024.10.1.039
ER -
Nguyen Thi Phuong Thanh and Cho Gyu Sung. (2024). Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model. Journal of Internet of Things and Convergence, 10(1), 39-45.
Nguyen Thi Phuong Thanh and Cho Gyu Sung. 2024, "Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model", Journal of Internet of Things and Convergence, vol.10, no.1 pp.39-45. Available from: doi:10.20465/KIOTS.2024.10.1.039
Nguyen Thi Phuong Thanh, Cho Gyu Sung "Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model" Journal of Internet of Things and Convergence 10.1 pp.39-45 (2024) : 39.
Nguyen Thi Phuong Thanh, Cho Gyu Sung. Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model. 2024; 10(1), 39-45. Available from: doi:10.20465/KIOTS.2024.10.1.039
Nguyen Thi Phuong Thanh and Cho Gyu Sung. "Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model" Journal of Internet of Things and Convergence 10, no.1 (2024) : 39-45.doi: 10.20465/KIOTS.2024.10.1.039
Nguyen Thi Phuong Thanh; Cho Gyu Sung. Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model. Journal of Internet of Things and Convergence, 10(1), 39-45. doi: 10.20465/KIOTS.2024.10.1.039
Nguyen Thi Phuong Thanh; Cho Gyu Sung. Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model. Journal of Internet of Things and Convergence. 2024; 10(1) 39-45. doi: 10.20465/KIOTS.2024.10.1.039
Nguyen Thi Phuong Thanh, Cho Gyu Sung. Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model. 2024; 10(1), 39-45. Available from: doi:10.20465/KIOTS.2024.10.1.039
Nguyen Thi Phuong Thanh and Cho Gyu Sung. "Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model" Journal of Internet of Things and Convergence 10, no.1 (2024) : 39-45.doi: 10.20465/KIOTS.2024.10.1.039
