AIMC Topic: Environmental Monitoring

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Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie.

Journal of environmental management Mar 17, 2025
Algal blooms, which have substantial adverse effects, are increasingly occurring worldwide in the context of global warming and eutrophication. Machine learning models (MLMs) are emerging as efficient and promising tools for predicting algal blooms. ...
Water Quality Chlorophyll Machine Learning Lakes Eutrophication Chlorophyll A Environmental Monitoring
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Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach.

Journal of environmental management Mar 17, 2025
Suspended sediment load (SSL) refers to sediment particles, such as silt and clay, that are suspended in water. It plays a critical role in hydrology and water quality management, influencing factors such as water quality, river erosion, sedimentatio...
Machine Learning Rivers Environmental Monitoring Extreme Learning Machines Geologic Sediments Water Quality
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Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods.

Environmental monitoring and assessment Mar 17, 2025
This study explores a low-cost sensor system for real-time algae bloom detection and water management. Harmful algal blooms (HABs) threaten water quality, ecosystems, and public health. Traditional detection methods, like satellite imagery and unmann...
Environmental Monitoring Support Vector Machine Remote Sensing Technology Harmful Algal Bloom Chlorophyll A Chlorophyll
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A hybrid vine copula-fuzzy model for groundwater level simulation under uncertainty.

Environmental monitoring and assessment Mar 17, 2025
Accurate simulation of groundwater level is crucial for the sustainable management of water resources. However, the numerous uncertainties in input data, simulation model parameters, and physical processes, as well as the dependency between system va...
Groundwater Fuzzy Logic Models, Theoretical Uncertainty Environmental Monitoring
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Automatic pre-screening of outdoor airborne microplastics in micrographs using deep learning.

Environmental pollution (Barking, Essex : 1987) Mar 14, 2025
Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of identifying potential particles in micrographs can significantly enhance the research and monitori...
Microplastics Environmental Monitoring Air Pollutants Neural Networks, Computer Deep Learning
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Integrated machine learning based groundwater quality prediction through groundwater quality index for drinking purposes in a semi-arid river basin of south India.

Environmental geochemistry and health Mar 14, 2025
The main objective of this study is to predict and monitor groundwater quality through the use of modern Machine Learning (ML) techniques. By employing ML techniques, the research effectively evaluates groundwater quality to forecast its future trend...
Water Quality Rivers Environmental Monitoring Water Pollutants, Chemical Drinking Water Machine Learning India Groundwater
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AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers.

Environment international Mar 13, 2025
The vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need...
Germany Animals Risk Assessment Artificial Intelligence Pharmaceutical Preparations Environmental Monitoring Water Pollutants, Chemical Rivers
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Integrating partial least square structural equation modelling and machine learning for causal exploration of environmental phenomena.

Environmental research Mar 11, 2025
Understanding the causes of environmental phenomena is crucial for promoting positive outcomes and mitigating negative ones. Partial least squares structural equation modelling (PLS-SEM) is becoming a valuable tool for evaluating causal relationships...
Lakes China Machine Learning Least-Squares Analysis Environmental Monitoring Harmful Algal Bloom Bayes Theorem
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Artificial Intelligence in Gas Sensing: A Review.

ACS sensors Mar 11, 2025
The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based i...
Humans Artificial Intelligence Environmental Monitoring Gases Machine Learning Deep Learning
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Enhancing short-term algal bloom forecasting through an anti-mimicking hybrid deep learning method.

Journal of environmental management Mar 10, 2025
Accurately predicting algal blooms remains a critical challenge due to their dynamic and non-stationary nature, compounded by high-frequency fluctuations and noise in monitoring data. Additionally, a common issue in time-series forecasting is data re...
Environmental Monitoring Chlorophyll Forecasting Eutrophication Deep Learning China
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