AIMC Topic: Harmful Algal Bloom

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Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization.

Scientific reports
Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic...

Tracking putative viruses and virus-host associations across distinct phases of a -dominated bloom.

mSystems
Viruses significantly impact microbial community composition and function. Yet their role in the fate of freshwater cyanobacterial harmful algal blooms (cHABs), an increasing threat to freshwater systems, remains poorly understood. Here, we address t...

Machine-Learning-Based Prediction of Algal Density Using Algal Volatile Organic Compounds for Bloom Early Warning.

Environmental science & technology
Harmful algal blooms (HABs) pose severe threats to aquatic ecosystems, yet rapid and accurate prediction of algal density remains challenging. As integrated metabolites are released throughout the algal growth, algal volatile organic compounds (AVOCs...

Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms.

Environmental science & technology
Real-time monitoring of phytoplankton in freshwater systems is critical for early detection of harmful algal blooms (HABs) to enable efficient response by water management agencies. This manuscript presents an image processing pipeline developed to a...

Time series forecasting of chlorophyll-a concentrations in the Chesapeake Bay.

Scientific reports
Declining water quality poses serious environmental and public health risks, with chlorophyll-a serving as a key biological indicator of harmful algal blooms. This study evaluates the use of a Long Short-Term Memory (LSTM) neural network to forecast ...

Climate change influences on algal bloom intensity in lakes in the Yangtze River Basin, China from 1985 to 2022.

Journal of hazardous materials
This study investigates the long-term influence of climate change on the spatiotemporal dynamics of harmful algal blooms in lakes larger than 10 km² across the Yangtze River Basin from 1985 to 2022. Using Landsat satellite imagery, we quantified bloo...

Harmful Algae Forecasting through an Ocean Data Justice Lens.

Environmental science & technology
Forecasting systems for harmful algal blooms (HABs) are becoming more common, as HAB monitoring is increasingly networked and aggregated at national and global scales. Ocean forecasting programs in other fields have had unintended consequences and ou...

Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method.

Journal of environmental management
The use of data driven deep learning models to predict and monitor Harmful Algal Blooms (HABs) has evolved over the years due to increasing technologies, availability of high frequency data, and statistical prowess. Despite the prowess of these data ...

Identification of key feature variables and prediction of harmful algal blooms in a water diversion lake based on interpretable machine learning.

Environmental research
Harmful algal blooms (HABs) as an increasing environmental problem in lakes, and water diversion has become a common and effective strategy for mitigating HABs. Early and accurate identification of the occurrence of HABs in lakes is essential for sci...

Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods.

Environmental monitoring and assessment
This study explores a low-cost sensor system for real-time algae bloom detection and water management. Harmful algal blooms (HABs) threaten water quality, ecosystems, and public health. Traditional detection methods, like satellite imagery and unmann...