AI-powered spatiotemporal imputation and prediction of chlorophyll-a concentration in coastal ecosystems.

ABSTRACT: Predicting spatiotemporal Chlorophyll-a (Chl_a) distributions is essential for diagnosing and analysing productivity and ecosystem health of coastal oceans. Yet, current tools remain inadequate for prognosing marine ecosystems through predicting spatiotemporal Chl_a distributions, particularly in the dynamic coastal ocean. Coupled physics-biogeochemical models struggle to resolve complex trophic interactions, while data-driven approaches are limited by incomplete satellite observations. We developed an advanced AI-powered spatiotemporal imputation and prediction (STIMP) model for predicting Chl_a in coastal ocean. STIMP adopts a novel paradigm that first imputes and subsequently predicts Chl_a across a broad spatiotemporal scale, resolving difficulties arising from incompletion, nonstation

SUBMITTER: Zhang F 

PROVIDER: S-EPMC12358577 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature