RT journal article
T1 A machine learning-based forecasting system of perishable cargo flow in maritime transport
A1 Moscoso López, José Antonio
A1 Urda, Daniel
A1 Ruiz Águilar, Juan Jesús
A1 González Enrique, Francisco Javier
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 Time series forecasting
K1 Machine learning
K1 Ensembles
K1 Perishable maritime cargo
K1 Decision support systems
AB The uncertainty cargo flow problem establishes a limitation in ports management where decisionmakingprocesses need accurate information of the future values. This work aims at predicting the futurevalues of Ro-Ro perishable cargo flow at the Port of Algeciras Bay using a machine learning-based forecasting system. Two datasets consisting of daily records of fresh fruits and vegetables between 2010 to2017 were analyzed. Additionally, these two--time series were pre-processed applying an exponentialmoving average method to obtain a smoothed version of the original ones. Multiple Linear Regression,Support Vector Machines, Long Short-Term Memory networks and an ensemble approach have been usedto build a forecasting system and obtain the future values of the perishable cargo. The results of the analysis showed how this machine learning-based system achieved 14.83% better performance rate than abaseline persistence model in terms of root mean squared error in the fresh fruits dataset and 11.3% better in the vegetables one. In general, models’ average performance rates are better using the smoothed version of the times series rather than the original ones.
PB Elsevier
SN 1872-8286
YR 2021
FD 2021
LK http://hdl.handle.net/10498/35214
UL http://hdl.handle.net/10498/35214
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026