Classification of Spanish ports using cluster analysis

El sistema portuario español es sumamente complejo y admite el estudio desde numerosos puntos de vista En este artículo se estudian los puertos según su actividad y sus características externas para la clasificación en agrupaciones. Para ello se han utilizado indicadores que reflejan la actividad portuaria y se han aplicado sobre las 28 Autoridades Portuarias españolas. Con estos indicadores se ha aplicado una metodología específica para a través del análisis de conglomerados (cluster) para averiguar cuáles son los agrupamientos que se producen. El análisis cluster se complementa con otros análisis estadísticos: análisis multivariante y componentes principales, para conocer qué indicadores son los más relevantes en las agrupaciones y cómo se comportan. Los resultados finales obtenidos muestran que este tipo de estudios estadísticos son apropiados para realizarse en el entorno portuario y que los agrupamientos reflejan correctamente la realidad portuaria.The Spanish port system is extremely complex and admits the study from many points of view. In this article the ports are studied from the point of view of classification in clusters according to their external characteristics. For this purpose, indicators have been used that reflect the port activity and have been applied on the 28 Spanish Port Authorities. With these indicators, a specific methodology has been applied through the analysis of clusters (cluster) to find out which clusters are produced. The cluster analysis is complemented by other analyzes (main components, multivariate analysis and individual indicators) to know which indicators are the most relevant in clusters and how they behave. The final results obtained show that this type of statistical studies are appropriate to be carried out in the port environment and that the groupings correctly reflect the port reality

Forecasting air pollutants using classification models: a case study in the Bay of Algeciras (Spain)

The main goal of this work is to obtain reliable predictions of pollutant concentrations related to maritime traffic (SO2, PM10, NO2, NOX, and NO) in the Bay of Algeciras, located in Andalusia, the south of Spain. Furthermore, the objective is to predict future air quality levels of the principal maritime traffic-related pollutants in the Bay of Algeciras as a function of the rest of the pollutants, the meteorological variables, and vessel data. In this sense, three scenarios were analysed for comparison, namely Alcornocales Park and the cities of La Linea and Algeciras. A database of hourly records of air pollution immissions, meteorological measurements in the Bay of Algeciras region and a database of maritime traffic in the port of Algeciras during the years 2017 to 2019 were used. A resampling procedure using a five-fold cross-validation procedure to assure the generalisation capabilities of the tested models was designed to compute the pollutant predictions with different classification models and also with artificial neural networks using different numbers of hidden layers and units. This procedure enabled appropriate and reliable multiple comparisons among the tested models and facilitated the selection of a set of top-performing prediction models. The models have been compared using several quality classification indexes such as sensitivity, specificity, accuracy, and precision. The distance (d(1)) to the perfect classifier (1, 1, 1, 1) was also used as a discriminant feature, which allowed for the selection of the best models. Concerning the number of variables, an analysis was conducted to identify the most relevant ones for each pollutant. This approach aimed to obtain models with fewer inputs, facilitating the design of an optimised monitoring network. These more compact models have proven to be the optimal choice in many cases. The obtained sensitivities in the best models were 0.98 for SO2, 0.97 for PM10, 0.82 for NO2 and NOX, and 0.83 for NO. These results demonstrate the potential of the models to forecast air pollution in a port city or a complex scenario and to be used by citizens and authorities to prevent exposure to pollutants and to make decisions concerning air quality.project RTI2018-098160-BI00;info:eu-repo/semantics/publishedVersio

Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

In this paper, the number of goods subject to inspection at European Border Inspections Post are predicted using a hybrid two-step procedure. A hybridization methodology based on integrating the data obtained from autoregressive integrated moving averages (SARIMA) model in the artificial neural network model (ANN) to predict the number of inspections is proposed. Several hybrid approaches are compared and the results indicate that the hybrid models outperform either of the models used separately. This methodology may become a powerful decision-making tool at other inspection facilities of international seaports or airports