AIMC Topic: Harmful Algal Bloom

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Predicting fish kills and toxic blooms in an intensive mariculture site in the Philippines using a machine learning model.

The Science of the total environment
Harmful algal blooms (HABs) that produce toxins and those that lead to fish kills are global problems that appear to be increasing in frequency and expanding in area. One way to help mitigate their impacts on people's health and livelihoods is to dev...

Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea.

International journal of environmental research and public health
In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and eco...

Four Major South Korea's Rivers Using Deep Learning Models.

International journal of environmental research and public health
Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to ...

The allelopathic effect and safety evaluation of 3,4-Dihydroxybenzalacetone on Microcystis aeruginosa.

Pesticide biochemistry and physiology
Harmful algal blooms (HABs) has been a serious problem in recent years, because of large quantities of cyanobacterial in eutrophic water. We studied the effects of 3,4-Dihydroxybenzalacetone (DBL) and other four compounds (vanillic acid, ferulic acid...

A novel application of an adaptable modeling approach to the management of toxic microalgal bloom events in coastal areas.

Harmful algae
Harmful algal blooms have been increasing in frequency in recent years, and attention has shifted from describing to modeling and trying to predict these phenomena, since in many cases they pose a risk to human health and coastal activities. Predicti...

The effect of the toxic dinoflagellate on the fitness of the calanoid copepod .

Harmful algae
Inshore and offshore waters of the Gulf of Maine (USA) have spring/summer harmful algal blooms (HABs) of the toxic dinoflagellate , which is responsible for paralytic shellfish poisoning (PSP) in humans. The calanoid copepod co-occurs with during t...

Key drivers of microcystin-producing cyanobacteria in South Korean eutrophic waters determined with data-driven models.

Journal of environmental management
The rise in cyanobacterial harmful algal blooms (CHABs), driven by eutrophication and climate change, necessitates understanding cyanotoxin conditions to mitigate risks. However, limited studies have explored the influencing factors of cyanobacterial...

Comprehensive Raman spectroscopy analysis for differentiating toxic cyanobacteria through multichannel 1D-CNNs and SHAP-based explainability.

Talanta
Cyanobacterial blooms pose significant environmental and public health risks due to the production of toxins that contaminate water sources and disrupt aquatic ecosystems. Rapid and accurate identification of cyanobacterial species is crucial for eff...

Climate-driven projections of cyanobacterial harmful algal bloom expansion in coastal waters.

The Science of the total environment
Cyanobacterial harmful algal blooms (CyanoHABs) in coastal waters are a growing ecological and environmental concern, especially in climate-vulnerable regions. While many studies have explored historical variations and short-term forecasting of Cyano...

Short-term spatial prediction of algal blooms in Lake Taihu via machine learning and GOCI observations.

Journal of environmental management
Harmful algal blooms are critical issues in eutrophic lakes worldwide. However, predicting the spatial distribution of algal blooms at the pixel level is still a challenge. In this study, floating algae cover (FAC) was used to extract algal coverage ...