Robust learning algorithms for capturing oceanic dynamics and transport of Noctiluca blooms using linear dynamical models.

ABSTRACT: The blooms of Noctiluca in the Gulf of Oman and the Arabian Sea have been intensifying in recent years, posing now a threat to regional fisheries and the long-term health of an ecosystem supporting a coastal population of nearly 120 million people. We present the results of a local-scale data analysis to investigate the onset and patterns of the Noctiluca blooms, which form annually during the winter monsoon in the Gulf of Oman and in the Arabian Sea. Our approach combines methods in physical and biological oceanography with machine learning techniques. In particular, we present a robust algorithm, the variable-length Linear Dynamic Systems (vLDS) model, that extracts the causal factors and latent dynamics at the local-scale along each individual drifter trajectory, and demonstrate its eff

SUBMITTER: Yan Y 

PROVIDER: S-EPMC6564007 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature