RT journal article
T1 A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities
A1 Ruiz Águilar, Juan Jesús
A1 Moscoso López, José Antonio
A1 Urda, Daniel
A1 González Enrique, Francisco Javier
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 maritime transport
K1 container forecasting
K1 support vector regression
K1 self-organizing maps
K1 machine learning
K1 hybrid models
AB 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.
PB MDPI
SN 2076-3417
YR 2020
FD 2020-12
LK http://hdl.handle.net/10498/24315
UL http://hdl.handle.net/10498/24315
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026