2026-06-02T04:16:12Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72502023-03-17T11:26:38Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Ruiz Aguilar, Juan Jesús Moscoso López, José Antonio Urda Muñoz, Daniel González Enrique, Francisco Javier Turias Domínguez, Ignacio J. 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