2026-04-28T08:53:22Zhttps://riubu.ubu.es/oai/request

oai: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 Informática Computer science 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. 2023-01-17T11:46:58Z 2023-01-17T11:46:58Z 2020-11 info:eu-repo/semantics/draft 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/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf MDPI