AIMC Topic: Harmful Algal Bloom

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Harmful Algae Forecasting through an Ocean Data Justice Lens.

Environmental science & technology Jun 10, 2025
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...
Oceans and Seas Biodiversity Harmful Algal Bloom Forecasting Environmental Monitoring
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Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method.

Journal of environmental management Apr 19, 2025
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 ...
Chlorophyll Chlorophyll A Environmental Monitoring Harmful Algal Bloom Deep Learning
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Identification of key feature variables and prediction of harmful algal blooms in a water diversion lake based on interpretable machine learning.

Environmental research Mar 28, 2025
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...
Machine Learning Environmental Monitoring Harmful Algal Bloom Lakes China
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Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods.

Environmental monitoring and assessment Mar 17, 2025
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...
Chlorophyll A Harmful Algal Bloom Chlorophyll Support Vector Machine Remote Sensing Technology Environmental Monitoring
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Integrating partial least square structural equation modelling and machine learning for causal exploration of environmental phenomena.

Environmental research Mar 11, 2025
Understanding the causes of environmental phenomena is crucial for promoting positive outcomes and mitigating negative ones. Partial least squares structural equation modelling (PLS-SEM) is becoming a valuable tool for evaluating causal relationships...
Least-Squares Analysis Harmful Algal Bloom Environmental Monitoring Bayes Theorem Machine Learning Lakes China
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Comprehensive Raman spectroscopy analysis for differentiating toxic cyanobacteria through multichannel 1D-CNNs and SHAP-based explainability.

Talanta Mar 7, 2025
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...
Deep Learning Spectrum Analysis, Raman Neural Networks, Computer Cyanobacteria Harmful Algal Bloom
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Dongting Lake algal bloom forecasting: Robustness and accuracy analysis of deep learning models.

Journal of hazardous materials Dec 5, 2024
Harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, prompting efforts to predict their occurrence for swift action by water management agencies. Despite the potential for high-precision forecasting through machine learning, t...
Deep Learning China Forecasting Lakes Water Quality Harmful Algal Bloom Models, Theoretical Environmental Monitoring
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Algal classification and Chlorophyll-a concentration determination using convolutional neural networks and three-dimensional fluorescence data matrices.

Environmental research Dec 2, 2024
In recent years, the frequency of harmful algal blooms has increased, leading to the release of large quantities of toxins and compounds that cause unpleasant odors and tastes, significantly compromising drinking water quality. Chlorophyll-a (Chl-a) ...
Harmful Algal Bloom Microcystis Chlorophyll Chlorophyll A Convolutional Neural Networks Environmental Monitoring Neural Networks, Computer Fluorescence Chlorella
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Development of a deep learning-based feature stream network for forecasting riverine harmful algal blooms from a network perspective.

Water research Nov 5, 2024
Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is r...
Deep Learning Environmental Monitoring Rivers Harmful Algal Bloom Republic of Korea Cyanobacteria Forecasting
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Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir.

Environmental research Oct 9, 2024
The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal bl...
Environmental Monitoring Water Quality Harmful Algal Bloom Cyanobacteria Deep Learning Neural Networks, Computer
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