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

Franceschini, S., Matte, F., D'Andrea, L., Di Nardi, A., Fiorentino, F., Garofalo, G., et al. (2019). Rummaging through the bin: Modelling marine litter distribution using Artificial Neural Networks. MARINE POLLUTION BULLETIN, 149, 110580 [10.1016/j.marpolbul.2019.110580].

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

Simone Franceschini;Lorenzo D'Andrea;Michele Scardi;Stefano Cataudella;Tommaso Russo
2019-01-01

Abstract

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
2019
Pubblicato
Rilevanza internazionale
Articolo
Esperti non anonimi
Settore BIO/07 - ECOLOGIA
English
Mediterranean; Machine learning; Self-organizing maps; Multilayer perceptron; MEDITS
https://www.sciencedirect.com/science/article/abs/pii/S0025326X19307283
Franceschini, S., Matte, F., D'Andrea, L., Di Nardi, A., Fiorentino, F., Garofalo, G., et al. (2019). Rummaging through the bin: Modelling marine litter distribution using Artificial Neural Networks. MARINE POLLUTION BULLETIN, 149, 110580 [10.1016/j.marpolbul.2019.110580].
Franceschini, S; Matte, F; D'Andrea, L; Di Nardi, A; Fiorentino, F; Garofalo, G; Scardi, M; Cataudella, S; Russo, T
Articolo su rivista
File in questo prodotto:
File Dimensione Formato  
2019-Franceschini et al.,MarPolBull.pdf

solo utenti autorizzati

Licenza: Copyright dell'editore
Dimensione 4.08 MB
Formato Adobe PDF
4.08 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/241119
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 23
social impact