Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean)

摘要

Algorithms based on a clever exploitation of artificial intelligence (AI) techniques are the key for modern multidisciplinaryapplications that are being developed in the last decades. AI approaches' ability of extractingrelevant information from data is essential to perform comprehensive studies in new multidisciplinary topicssuch as ecological informatics. For example, improving knowledge on cetaceans' distribution patterns enablesthe acquisition of a strategic expertise for developing tools aimed to the preservation of the marine environment.In this paper we present an innovative approach, based on Random Forest and RUSBoost, aimed to define predictivemodels for presence/absence and abundance estimation of two classes of cetaceans: the striped dolphinStenella coeruleoalba and the common bottlenose dolphin Tursiops truncatus. Sightings data from 2009 to 2017have been collected and enriched by geo-morphological and meteorological data in order to build a comprehensivedataset of real observations used to train and validate the proposed algorithms. Results in terms of classificationand regression accuracy demonstrate the feasibility of the proposed approach and suggest the application of suchartificial intelligence based techniques to larger datasets, with the aim of enabling large scale studies as well asimproving knowledge on data deficient species.