Identifying characteristics of seaports for environmental benchmarks based on meta-learning

In this paper we discuss a model which classifies any seaport in the context of environmental management system standards as leader, follower and average user. Identification of this status can assist Port Authorities (PAs) in making decisions concerned with finding collaborating seaport partners using clear environmental benchmarks. This paper demonstrates the suitability of meta-learning for small datasets to assist pre-selection of base-algorithms and automatic parameterization. The method is suitable for small number of observations with many attributes closely related with potential issues concerning environmental management programs on seaports. The variables in our dataset cover main aspects such as reducing air emissions, improving water quality and minimizing impacts of growth. We consider this model will be suitable for Port authorities (PAs) interested in effective and efficient methods of knowledge discovery to be able to gain the maximum advantage of benchmarking processes within partner ports. As well as for practitioners and non-expert users who want to construct a reliable classification process and reduce the evaluation time of data processing for environmental benchmarking.