AIMC Topic: Eutrophication

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Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm.

The Science of the total environment Mar 8, 2021
Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-...
Chlorophyll A Environmental Monitoring Chlorophyll Lakes Artificial Intelligence Algorithms China Eutrophication Ecosystem
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Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain).

International journal of environmental research and public health Feb 13, 2020
The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt ch...
Machine Learning Algorithms Environmental Monitoring Spain Ecosystem Eutrophication
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Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes.

Environmental science and pollution research international Sep 3, 2019
Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency...
Nitrogen Phosphorus Water Quality Environmental Monitoring Cluster Analysis United States Environmental Protection Agency Chlorophyll Chlorophyll A Algorithms Ecosystem Lakes United States Eutrophication Artificial Intelligence
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Transfer learning for neural network model in chlorophyll-a dynamics prediction.

Environmental science and pollution research international Aug 13, 2019
Neural network models have been used to predict chlorophyll-a concentration dynamics. However, as model generalization ability decreases, (i) the performance of the models gradually decreases over time; (ii) the accuracy and performance of the models...
Kinetics Chlorophyll A China Predictive Value of Tests Machine Learning Eutrophication Neural Networks, Computer
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Comprehensive Eutrophication Assessment Based on Fuzzy Matter Element Model and Monte Carlo-Triangular Fuzzy Numbers Approach.

International journal of environmental research and public health May 19, 2019
Evaluating the eutrophication level of lakes with a single method alone is challenging since uncertain, fuzzy, and complex processes exist in eutrophication evaluations. The parameters selected for assessing eutrophication include chlorophyII-a, chem...
Lakes China Eutrophication Monte Carlo Method Uncertainty Fuzzy Logic Nitrogen Water Phosphorus Models, Theoretical
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Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

Environmental science and pollution research international Sep 30, 2017
In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A li...
Global Warming Gasoline Humans Environmental Pollution Neural Networks, Computer Iran Eutrophication Food-Processing Industry Tea
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Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm.

Marine pollution bulletin Apr 23, 2017
The development of techniques for real-time monitoring of the eutrophication status of coastal waters is of great importance for realizing potential cost savings in coastal monitoring programs and providing timely advice for marine health management....
Algorithms Seawater Eutrophication Support Vector Machine
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Applying the Back-Propagation Neural Network model and fuzzy classification to evaluate the trophic status of a reservoir system.

Environmental monitoring and assessment Aug 13, 2015
The trophic state index, and in particular, the Carlson Trophic State Index (CTSI), is critical for evaluating reservoir water quality. Despite its common use in evaluating static water quality, the reliability of the CTSI may decrease when water tur...
Conservation of Natural Resources Eutrophication Water Resources Environmental Monitoring Fuzzy Logic Neural Networks, Computer Water Quality
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On-water remote monitoring robotic system for estimating the patch coverage of Anabaena sp. filaments in shallow water.

Environmental science. Processes & impacts May 12, 2015
An on-water remote monitoring robotic system was developed for indirectly estimating the relative density of marine cyanobacteria blooms at the subtidal sandy-rocky beach in Balandra Cove, Baja California Sur, Mexico. The system is based on an unmann...
Seawater Anabaena Environmental Monitoring Cyanobacteria Robotics Water Pollution Eutrophication
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A novel hybrid machine learning approach for accurate retrieval of ocean surface chlorophyll-a across oligotrophic to eutrophic waters.

Environmental research Aug 15, 2025
Accurate assessment of chlorophyll a (Chla) concentration distribution and variations is significant for environmental monitoring and ecological research. However, the inversion of Chla in different optical types of water bodies can only be achieved ...
Machine Learning Algorithms Chlorophyll Chlorophyll A Oceans and Seas Environmental Monitoring Seawater Eutrophication
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