AIMC Topic: Lakes

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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 ...
Anti-Bacterial Agents Water Genes, Bacterial Bacteriophages Lakes Deep Learning
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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...
Chlorophyll A Rivers Chlorophyll Environmental Monitoring Support Vector Machine Nitrogen Phosphorus Lakes China Eutrophication
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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 ...
Ecosystem Lakes Neural Networks, Computer Machine Learning Causality
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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 ...
Environmental Monitoring Water Quality Communicable Disease Control Lakes Deep Learning COVID-19 Ecosystem Humans
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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 China Harmful Algal Bloom Environmental Monitoring Lakes
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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 ...
Lakes Deep Learning Water Quality Environmental Monitoring Remote Sensing Technology
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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...
Support Vector Machine Magnetic Phenomena Humans Animals Lakes Neural Networks, Computer Machine Learning
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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-...
Lakes China Chlorophyll A Artificial Intelligence Ecosystem Environmental Monitoring Algorithms Eutrophication Chlorophyll
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Sustainable management of water demand using fuzzy inference system: a case study of Kenyir Lake, Malaysia.

Environmental science and pollution research international Jan 6, 2021
Sustainable water demand management has become a necessity to the world since the immensely growing population and development have caused water deficit and groundwater depletion. This study aims to overcome water deficit by analyzing water demand at...
Fuzzy Logic Malaysia Water Humans Groundwater Lakes
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