AIMC Topic: Lakes

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Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich.

Water research Dec 10, 2023
Three-dimensional lake hydrodynamic model is a powerful tool widely used to assess hydrological condition changes of lake. However, its computational cost becomes problematic when forecasting the state of large lakes or using high-resolution simulati...
Neural Networks, Computer Deep Learning Hydrodynamics Computer Simulation Lakes
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Artificial intelligence to explain the variables that favor the cyanobacteria steady-state in tropical ecosystems: A Bayeasian network approach.

Anais da Academia Brasileira de Ciencias Dec 1, 2023
The steady-state is a situation of little variability of species dominance and total biomass over time. Maintenance of cyanobacteria are often observed in tropical and eutrophic ecosystems and can cause imbalances in aquatic ecosystem. Bayeasian netw...
Ecosystem Lakes Artificial Intelligence Cyanobacteria Biomass Eutrophication
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Identifying ARG-carrying bacteriophages in a lake replenished by reclaimed water using deep learning techniques.

Water research Nov 11, 2023
As important mobile genetic elements, phages support the spread of antibiotic resistance genes (ARGs). Previous analyses of metaviromes or metagenome-assembled genomes (MAGs) failed to assess the extent of ARGs transferred by phages, particularly in ...
Bacteriophages Water Genes, Bacterial Lakes Deep Learning Anti-Bacterial Agents
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Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm.

Environmental monitoring and assessment May 20, 2023
Outbreaks of planktonic algae seriously affect the water quality of rivers and are difficult to control. Based on the analysis of the temporal and spatial variation characteristics of environmental factors, this study uses a support vector machine re...
Eutrophication Environmental Monitoring Support Vector Machine Lakes China Chlorophyll A Rivers Chlorophyll Nitrogen Phosphorus
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Decomposing predictability to identify dominant causal drivers in complex ecosystems.

Proceedings of the National Academy of Sciences of the United States of America Oct 10, 2022
Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods ...
Causality Ecosystem Lakes Neural Networks, Computer Machine Learning
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Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach.

Water research May 14, 2022
The rapid emergence of deep learning long-short-term-memory (LSTM) technique presents a promising solution to algal bloom forecasting. However, the discontinuous and non-stationary processes within algal dynamics still largely limit the functions of ...
Lakes Deep Learning Forecasting Eutrophication Neural Networks, Computer
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Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images.

Water research Mar 1, 2022
Information regarding water clarity at large spatiotemporal scales is critical for understanding comprehensive changes in the water quality and status of ecosystems. Previous studies have suggested that satellite observation is an effective means of ...
Deep Learning Environmental Monitoring Water Quality Communicable Disease Control Humans Ecosystem COVID-19 Lakes
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A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China.

Harmful algae Jan 28, 2022
Cyanobacterial Harmful Algae Blooms (CyanoHABs) in the eutrophic lakes have become a global environmental and ecological problem. In this study, a CNN-LSTM integrated model for predicting the CyanoHABs area was proposed and applied to the prediction ...
Deep Learning Lakes China Environmental Monitoring Harmful Algal Bloom
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Deep learning-based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images.

Environmental science and pollution research international Aug 18, 2021
Water transparency is a key indicator of water quality as it reflects the turbidity and eutrophication in lakes and reservoirs. To carry out remote sensing monitoring of water transparency rapidly and intelligently, deep learning technology was used ...
Water Quality Environmental Monitoring Remote Sensing Technology Lakes Deep Learning
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Magnetic properties and its application in the prediction of potentially toxic elements in aquatic products by machine learning.

The Science of the total environment Apr 20, 2021
Magnetic measurement was provided to substitute for time-consuming conventional methods for determination of potentially toxic elements. Both the concentrations of 12 elements and 9 magnetic parameters were determined in 700 muscle tissue samples fro...
Animals Humans Support Vector Machine Neural Networks, Computer Lakes Machine Learning Magnetic Phenomena
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