AIMC Topic: Chlorophyll A
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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." ...
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 ...
Assessing the impacts of cascade reservoirs on Pearl River environmental status using machine learning and satellite-derived chlorophyll-a concentrations.
Journal of environmental management
Apr 18, 2025
Rivers play a crucial role in in global matter cycling and energy flow, contributing significantly to biogeochemical cycles and the development of human civilization. Reservoirs, as prevalent artificial water bodies, modify river flow and impact ener...
Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie.
Journal of environmental management
Mar 17, 2025
Algal blooms, which have substantial adverse effects, are increasingly occurring worldwide in the context of global warming and eutrophication. Machine learning models (MLMs) are emerging as efficient and promising tools for predicting algal blooms. ...
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...
Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803.
BMC biotechnology
Mar 15, 2025
BACKGROUND: Natural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and phycocyanin. The current study used the Synechocystis sp. PCC 6803 to examine how abiotic stress conditions, such as low temperature as well as high l...
Change analysis of surface water clarity in the Persian Gulf and the Oman Sea by remote sensing data and an interpretable deep learning model.
Environmental science and pollution research international
Feb 18, 2025
The health of an ecosystem and the quality of water can be determined by the clarity of the water. The Persian Gulf and Oman Sea have a unique ecosystem, and monitoring their water clarity is necessary for sustainable development. Here, various crite...
Integrating machine learning and remote sensing for long-term monitoring of chlorophyll-a in Chilika Lagoon, India.
Environmental monitoring and assessment
Dec 26, 2024
Chlorophyll-a (Chla) is recognized as a key indicator of water quality and ecological health in aquatic ecosystems, offering valuable insights into ecosystem dynamics and changes over time. This study aimed to to develop and validate a robust ML mode...
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) ...
Long-term water quality assessment in coastal and inland waters: An ensemble machine-learning approach using satellite data.
Marine pollution bulletin
Nov 16, 2024
Accurate estimation of coastal and in-land water quality parameters is important for managing water resources and meeting the demand of sustainable development goals. The water quality monitoring based on discrete water sample analysis is limited to ...
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