2026-04-28T08:56:30Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72502023-03-17T11:26:38Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Ruiz Aguilar, Juan Jesús
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600
Moscoso López, José Antonio
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Urda Muñoz, Daniel
753
600
0000-0003-2662-798X
González Enrique, Francisco Javier
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600
Turias Domínguez, Ignacio J.
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600
2023-01-17T11:46:58Z
2023-01-17T11:46:58Z
2020-11
http://hdl.handle.net/10259/7250
10.3390/app10238326
2076-3417
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.
This research was funded by MICINN (Ministerio de Ciencia e Innovación-Spain), grant number RTI2018-098160-B-I00.
application/pdf
eng
MDPI
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/
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Maritime transport
Container forecasting
Support vector regression
Self-organizing maps
Machine learning
Hybrid models
Informática
Computer science
A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities
Applied Sciences
10
23
info:eu-repo/semantics/draft
THUMBNAIL
Urda-as_2020.pdf.jpg
Urda-as_2020.pdf.jpg
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LICENSE
license.txt
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ORIGINAL
Urda-as_2020.pdf
Urda-as_2020.pdf
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10259/7250
oai:riubu.ubu.es:10259/7250
2023-03-17 12:26:38.066
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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