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

S Franceschini1, F Mattei1, L D'Andrea1

  • 1Laboratory of Experimental Ecology and Aquaculture, Department of Biology, University of Rome Tor Vergata, via della Ricerca Scientifica snc, 00133 Rome, Italy; CoNISMa, Piazzale Flaminio, 9, 00196 Rome, Italy.

Marine Pollution Bulletin
|September 24, 2019
PubMed
Summary
This summary is machine-generated.

Artificial Neural Networks can model marine litter distribution and quantity on the seabed. Machine learning offers a promising approach to assess marine litter issues and identify hotspots in marine environments.

Keywords:
MEDITSMachine learningMediterraneanMultilayer perceptronSelf-organizing maps

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Area of Science:

  • Marine Biology
  • Environmental Science
  • Data Science

Background:

  • Marine litter poses significant ecological, social, and economic threats.
  • Accurate assessment of marine litter hotspots and accumulation zones remains a challenge.
  • Predictive models for seabed marine litter distribution are currently limited.

Purpose of the Study:

  • To model the influence of environmental factors on marine litter distribution.
  • To estimate the total quantity of marine litter on the seabed in the Central Mediterranean Sea.
  • To evaluate the efficacy of Artificial Neural Networks (ANNs) for marine litter assessment.

Main Methods:

  • Utilized Artificial Neural Networks (ANNs), including Self-Organing Maps (SOMs) and Multilayer Perceptrons (MLPs).
  • Employed environmental descriptors to model the relationship with marine litter density.
  • Developed an MLP model for quantifying regional seabed marine litter.

Main Results:

  • Self-Organizing Maps identified key environmental descriptors influencing marine litter density.
  • The Multilayer Perceptron model demonstrated efficiency in estimating regional seabed litter quantities.
  • Results indicate a strong correlation between environmental factors and litter distribution.

Conclusions:

  • Machine learning, specifically ANNs, is a viable and effective approach for assessing marine litter.
  • This study provides a novel method for predicting marine litter hotspots and quantities.
  • The findings support the use of computational models in marine conservation and management efforts.