AIMC Topic: Eutrophication
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Cyanobacteria hot spot detection integrating remote sensing data with convolutional and Kolmogorov-Arnold networks.
The Science of the total environment
Jan 6, 2025
Prompt and accurate monitoring of cyanobacterial blooms is essential for public health management and understanding aquatic ecosystem dynamics. Remote sensing, in particular satellite observations, presents a good alternative for continuous monitorin...
The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential.
Marine pollution bulletin
Dec 30, 2024
Recent advancements in data analytics, predictive modeling, and optimization have highlighted the potential of integrating algal blooms (ABs) with Industry 4.0 technologies. Among these innovations, digital twins (DT) have gained prominence, driven b...
Are more data always better? - Machine learning forecasting of algae based on long-term observations.
Journal of environmental management
Dec 2, 2024
Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important...
Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches.
Water research
Nov 29, 2024
The eutrophication of lakes and the subsequent algal blooms have become significant environmental issues of global concern in recent years. With ongoing global warming and intensifying human activities, water quality trends in lakes worldwide varied ...
Leveraging explainable machine learning for enhanced management of lake water quality.
Journal of environmental management
Oct 13, 2024
Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km) subtropical lake as a...
Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique.
The Science of the total environment
Sep 29, 2024
Eutrophication is a major cause of water quality degradation in South Korea, owing to severe algal blooms. To manage eutrophication, the South Korean government provided the Trophic State Index (TSIko), which was revised according to Carlson's TSI. T...
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...
Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.
The Science of the total environment
Aug 10, 2024
Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task ...
Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic.
The Science of the total environment
Jul 4, 2024
Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This s...
Exploring spatiotemporal patterns of algal cell density in lake Dianchi with explainable machine learning.
Environmental pollution (Barking, Essex : 1987)
Jun 18, 2024
The escalating global occurrence of algal blooms poses a growing threat to ecosystem services. In this study, the spatiotemporal heterogeneity of water quality parameters was leveraged to partition Lake Dianchi into three clusters. Considering water ...
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