Rummaging through the bin: Modelling marine litter distribution using Artificial Neural Networks

Marine litter has significant ecological, social and economic impacts, ultimately raising welfare and conservation concerns. Assessing marine litter hotspots or inferring potential areas of accumulation are challenging topics of marine research. Nevertheless, models able to predict the distribution of marine litter on the seabed are still limited. In this work, a set of Artificial Neural Networks were trained to both model the effect of environmental descriptors on litter distribution and estimate the amount of marine litter in the Central Mediterranean Sea. The first goal involved the use of self-organizing maps in order to highlight the importance of environmental de- scriptors in affecting marine litter density. The second goal was achieved by developing a multilayer perceptron model, which proved to be an efficient method to estimate the regional quantity of seabed marine litter. Results demonstrated that machine learning could be a suitable approach in the assessment of the marine litter issue