2026-06-02T04:15:18Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72502023-03-17T11:26:38Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities
Ruiz Aguilar, Juan Jesús
Moscoso López, José Antonio
Urda Muñoz, Daniel
González Enrique, Francisco Javier
Turias Domínguez, Ignacio J.
Maritime transport
Container forecasting
Support vector regression
Self-organizing maps
Machine learning
Hybrid models
An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor
to improve planning operations and resource allocation. This study proposes a hybrid approach
to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a
three-step procedure, combining the strengths of linear and non-linear models and the capability
of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series
into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages
(SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of
each cluster. These values are finally used as inputs of a support vector regression (SVR) model
together with the historical data of the cluster. The final prediction result integrates the prediction
results of each cluster. The experimental results showed that the proposed model provided accurate
prediction results and outperforms the rest of the models tested. The proposed model can be used as
an automatic decision-making tool by seaport management due to its capacity to plan resources in
advance, avoiding congestion and time delays.
2023-01-17
2023-01-17
2020-11
http://hdl.handle.net/10259/7250
10.3390/app10238326
2076-3417
eng
Applied sciences. 2020, V. 10, n. 23, e8326
https://doi.org/10.3390/app10238326
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098160-B-I00/ES/DEEP LEARNING IN AIR POLLUTION FORECASTING/
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Atribución 4.0 Internacional
MDPI