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

Juan Jesús Ruiz-Aguilar,Daniel Urda,Javier González-Enrique,José Antonio Moscoso-López,Ignacio Turias

doi:10.3390/app10238326

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.

Highlights

Over the last decades, ports have played an important role in international trade and most of the overseas shipping of products is aboard deep-sea container vessels [1]
The results of the proposed hybrid seasonal autoregressive integrated moving averages (SARIMA)-self-organizing map (SOM)-support vector machines for regression (SVR) models to predict the daily number of containers passing through the Border Inspection Posts (BIPs) of the Port of Algeciras Bay are presented and discussed
These results are those obtained in the third step of the proposed procedure and they were assessed and compared with those achieved with the single SVR, the combined SOM-SVR, and the hybrid SARIMA-SVR models

Summary

Introduction

Ports have played an important role in international trade and most of the overseas shipping of products is aboard deep-sea container vessels [1]. The increase in traffic of goods and the European unification has led to consider the enhancement of security at border crossings of the European Union. In this sense, the Border Inspection Posts (BIPs) were created in order to guarantee the security at border crossings and the quality of the import-export goods. The sustained growth in the worldwide exchange of goods is creating the need for further inspections resulting in congestion and high load-peaks within the sanitary facilities. This causes time delays and higher costs in the supply chain. In order to avoid time delays and congestion in the sanitary facilities, the port management should be able to accurately

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