AIMC Topic: Phytoplankton
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Holistic characterization of water quality parameters to understand the ecological impacts of eutrophication.
Marine pollution bulletin
May 17, 2025
The naturally oligotrophic waters of the Florida Keys, U.S.A., can be impacted by small increases in nutrients, which are reflected by increased phytoplankton productivity and over time, produce hypoxic conditions, a process called "eutrophication." ...
Enhancing tree-based machine learning for chlorophyll-a prediction in coastal seawater through spatiotemporal feature integration.
Marine environmental research
Apr 24, 2025
The excessive growth of phytoplankton in water can deplete oxygen, release toxins, harm aquatic life, cause economic losses, and threaten coastal residents. Accurately predicting phytoplankton levels is crucial for safeguarding marine life and coasta...
Design of a fractional-order environmental toxin-plankton system in aquatic ecosystems: A novel machine predictive expedition with nonlinear autoregressive neuroarchitectures.
Water research
Apr 12, 2025
Artificial intelligence has transformed both plankton dynamics and hazardous material management under toxic environments by enhanced hazard prediction in detecting how toxins affect plankton population and potentially uncovering greater depth of eco...
Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea.
Scientific reports
Jan 24, 2025
Phytoplankton blooms exhibit varying patterns in timing and number of peaks within ecosystems. These differences in blooming patterns are partly explained by phytoplankton:nutrient interactions and external factors such as temperature, salinity and l...
Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images.
Scientific reports
Jan 21, 2025
Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion...
Machine learning assessment of dredging impacts on the phytoplankton community on the Brazilian equatorial margin: A multivariate analysis.
Environmental pollution (Barking, Essex : 1987)
Jan 10, 2025
Dredging in estuarine systems significantly impacts phytoplankton communities, with suspended particulate matter (SPM) and dissolved aluminum (Al) serving as indicators of disturbance intensity. This study assessed the effects of dredging in the São ...
Explainable machine learning for predicting diarrhetic shellfish poisoning events in the Adriatic Sea using long-term monitoring data.
Harmful algae
Sep 23, 2024
In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful algal blooms. By analysing a newly created 28-year dataset containing records of toxic phyto...
Exploring the response and prediction of phytoplankton to environmental factors in eutrophic marine areas using interpretable machine learning methods.
The Science of the total environment
Aug 17, 2024
Coastal marine areas are frequently affected by human activities and face ecological and environmental threats, such as algal blooms and climate change. The community structure of phytoplankton-primary producers in marine ecosystems-is highly sensiti...
Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds.
Journal of environmental management
May 9, 2024
Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, espe...
Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.
PeerJ
May 8, 2024
Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, ., can retard their growth. With a...
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