Marcus Schulz, Michael Matthies, Artificial neural networks for modeling
time series of beach litter in the southern North Sea, Marine
Environmental Research, Available online 28 March 2014, ISSN 0141-1136,
http://dx.doi.org/10.1016/j.marenvres.2014.03.014.
(http://www.sciencedirect.com/science/article/pii/S0141113614000634)
Abstract: In European marine waters, existing monitoring programs of
beach litter need to be improved concerning litter items used as
indicators of pollution levels, efficiency, and effectiveness. In order
to ease and focus future monitoring of beach litter on few important
litter items, feed-forward neural networks consisting of three layers
were developed to relate single litter items to general categories of
marine litter. The neural networks developed were applied to seven
beaches in the southern North Sea and modeled time series of five
general categories of marine litter, such as litter from fishing,
shipping, and tourism. Results of regression analyses show that general
categories were predicted significantly moderately to well. Measured and
modeled data were in the same order of magnitude, and minima and maxima
overlapped well. Neural networks were found to be eligible tools to
deliver reliable predictions of marine litter with low computational
effort and little input of information.
Keywords: Marine litter; back propagation; neural network; regression
analysis; beach litter category; source category