Marine Litter Stormy Wash-Outs: Developing the Neural Network to Predict Them

Abstract

Observations show that after stormy events, anthropogenic litter is washed ashore for short periods of time, providing the opportunity to collect and remove it from the environment. However, water dynamics in sea coastal zones during and after storms are very complicated, and the transport properties of litter items are very diverse; thus, predicting litter wash-outs using classical numerical models is challenging. We analyze meteorological and hydrophysical conditions in the Baltic Sea coastal zone to further use the obtained data as a training sequence for an artificial neural network (ANN). Analysis of the physical processes behind large litter wash-outs links open-source meteorological (wind speed and direction) and hydrodynamic reanalysis (surface wave parameters) data to the time and location of these wash-outs. A detailed analysis of 25 cases of wash-outs observed at the shore of the Sambian Peninsula was performed. The importance of the duration of the storm and its subsiding phase was revealed. An ANN structure is proposed for forecasting marine debris wash-outs as the first step in the creation of a neural network-based tool for managers and beach cleaners, helping to plan effective measures to remove plastics and other anthropogenic contaminants from the marine environment.