AIMC Topic: Lakes

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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-...
Artificial Intelligence Algorithms Ecosystem Environmental Monitoring Chlorophyll Chlorophyll A Eutrophication Lakes China
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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...
Groundwater Fuzzy Logic Malaysia Humans Lakes Water
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Application of machine learning to identify predators of stocked fish in Lake Ontario: using acoustic telemetry predation tags to inform management.

Journal of fish biology Nov 3, 2020
Understanding predator-prey interactions and food web dynamics is important for ecosystem-based management in aquatic environments, as they experience increasing rates of human-induced changes, such as the addition and removal of fishes. To quantify ...
Ecosystem Lakes Ontario Machine Learning Animals Fisheries Animal Identification Systems Trout Food Chain Salmonidae Predatory Behavior Telemetry Acoustics
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Lake water-level fluctuation forecasting using machine learning models: a systematic review.

Environmental science and pollution research international Sep 25, 2020
Lake water-level fluctuation is a complex and dynamic process, characterized by high stochasticity and nonlinearity, and difficult to model and forecast. In recent years, applications of machine learning (ML) models have yielded substantial progress ...
Neural Networks, Computer Machine Learning Forecasting Lakes Support Vector Machine
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Modelling of ecological status of Polish lakes using deep learning techniques.

Environmental science and pollution research international Sep 22, 2020
Since 2000, after the Water Framework Directive came into force, aquatic ecosystems' bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered compre...
Deep Learning Environmental Monitoring Phytoplankton Lakes Poland Chlorophyll A Ecosystem
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Photoautotrophic picoplankton - a review on their occurrence, role and diversity in Lake Balaton.

Biologia futura Jul 31, 2020
Occurrence of the smallest phototrophic microorganisms (photoautotrophic picoplankton, APP) in Lake Balaton was discovered in the early 1980s. This triggered a series of systematic studies on APP and resulted in the setting of a unique long-term pico...
Phytoplankton Lakes Hungary
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Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM).

Journal of environmental management Dec 3, 2019
Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for ...
Groundwater Artificial Intelligence Data Interpretation, Statistical Environmental Monitoring Lakes Fuzzy Logic
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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...
Chlorophyll Chlorophyll A United States Environmental Protection Agency Phosphorus Water Quality Eutrophication Lakes United States Environmental Monitoring Artificial Intelligence Cluster Analysis Algorithms Nitrogen Ecosystem
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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 Water Eutrophication Phosphorus Models, Theoretical Monte Carlo Method Fuzzy Logic Uncertainty Nitrogen
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Magnetic γ-cyclodextrin polymer with compatible cavity promote the magnetic solid-phase extraction of microcystins in water samples.

Analytica chimica acta Dec 21, 2018
Microcystins (MCs), which are produced by eruptive cyanobacteria, seriously threaten the health of humans for their poisonousness. Herein, a facile strategy was introduced to synthesize the magnetic γ-cyclodextrin polymer (FeO@PDA@γ-CDP) composite an...
Magnets Microspheres Seawater Adsorption Ferrosoferric Oxide Polymers Hydrogen-Ion Concentration Lakes Time Factors Water Cellulose Solid Phase Extraction Microcystins Indoles Chromatography, High Pressure Liquid Cyclodextrins Tandem Mass Spectrometry
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