Satellite-based feature extraction and multivariate time-series
  prediction of biotoxin contamination in shellfish
Sergio Tavares Pedro R. Costa Ludwig Krippahl Marta B. Lopes
Shellfish production constitutes an important sector for the economy of many
Portuguese coastal regions, yet the challenge of shellfish biotoxin
contamination poses both public health concerns and significant economic risks.
Thus, predicting shellfish contamination levels holds great potential for
enhancing production management and safeguarding public health. In our study,
we utilize a dataset with years of Sentinel-3 satellite imagery for marine
surveillance, along with shellfish biotoxin contamination data from various
production areas along Portugal's western coastline, collected by Portuguese
official control. Our goal is to evaluate the integration of satellite data in
forecasting models for predicting toxin concentrations in shellfish given
forecasting horizons up to four weeks, which implies extracting a small set of
useful features and assessing their impact on the predictive models. We framed
this challenge as a time-series forecasting problem, leveraging historical
contamination levels and satellite images for designated areas. While
contamination measurements occurred weekly, satellite images were accessible
multiple times per week. Unsupervised feature extraction was performed using
autoencoders able to handle non-valid pixels caused by factors like cloud
cover, land, or anomalies. Finally, several Artificial Neural Networks models
were applied to compare univariate (contamination only) and multivariate
(contamination and satellite data) time-series forecasting. Our findings show
that incorporating these features enhances predictions, especially beyond one
week in lagoon production areas (RIAV) and for the 1-week and 2-week horizons
in the L5B area (oceanic). The methodology shows the feasibility of integrating
information from a high-dimensional data source like remote sensing without
compromising the model's predictive ability.