AI Medical Compendium Topic:
Environmental Monitoring

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Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing.

Waste management (New York, N.Y.)
Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, ...

Predicting the governing factors for the release of colloidal phosphorus using machine learning.

Chemosphere
Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent th...

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines.

Marine pollution bulletin
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due...

Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning.

Biosensors & bioelectronics
Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests tha...

Machine learning in soil nutrient dynamics of alpine grasslands.

The Science of the total environment
As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional ...

Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques.

Marine pollution bulletin
Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning ...

Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach.

Chemosphere
The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil sp...

Analyzing variation of water inflow to inland lakes under climate change: Integrating deep learning and time series data mining.

Environmental research
The alarming depletion of global inland lakes in recent decades makes it essential to predict water inflow from rivers to lakes (WIRL) trend and unveil the dominant influencing driver, particularly in the context of climate change. The raw time serie...

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.

The Science of the total environment
Short-term exposure to ground-level ozone (O) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O forecasting to mitigate these risks, focusing on ...

The use of vibrational spectroscopy and supervised machine learning for chemical identification of plastics ingested by seabirds.

Journal of hazardous materials
Plastic pollution is now ubiquitous in the environment and represents a growing threat to wildlife, who can mistake plastic for food and ingest it. Tackling this problem requires reliable, consistent methods for monitoring plastic pollution ingested ...