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A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities

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URI: http://hdl.handle.net/10498/24315

DOI: 10.3390/app10238326

ISSN: 2076-3417

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Author/s
Ruiz Aguilar, Juan JesúsAuthority UCA; Moscoso López, José AntonioAuthority UCA; Urda, Daniel; González Enrique, Francisco JavierAuthority UCA; Turias Domínguez, Ignacio JoséAuthority UCA
Date
2020-12
Department
Ingeniería Industrial e Ingeniería Civil; Ingeniería Informática
Source
Appl. Sci. 2020, 10(23), 8326
Abstract
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.
Subjects
maritime transport; container forecasting; support vector regression; self-organizing maps; machine learning; hybrid models
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  • Articulos Científicos Ing. Ind. [32]
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Atribución 4.0 Internacional
This work is under a Creative Commons License Atribución 4.0 Internacional

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