AI Medical Compendium Topic:
Environmental Monitoring

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Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques.

Sensors (Basel, Switzerland)
Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor s...

Prediction of the outlet flow temperature in a flat plate solar collector using artificial neural network.

Environmental monitoring and assessment
In the current research, the efficiency of a solar flat plate collector (SFPC) was examined experimentally, while the system was modeled with an artificial neural network (ANN) under semi-arid weather conditions of Rafsanjan, Iran. Based on the backp...

A comparison of two remotely operated vehicle (ROV) survey methods used to estimate fish assemblages and densities around a California oil platform.

PloS one
Offshore oil and gas platforms have a finite life of production operations. Once production ceases, decommissioning options for the platform are assessed. The role that a platform's jacket plays as fish habitat can inform the decommissioning decision...

PM2.5 concentration modeling and prediction by using temperature-based deep belief network.

Neural networks : the official journal of the International Neural Network Society
Air quality prediction is a global hot issue, and PM is an important factor affecting air quality. Due to complicated causes of formation, PM prediction is a thorny and challenging task. In this paper, a novel deep learning model named temperature-ba...

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction.

Environmental science and pollution research international
Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in r...

Machine learning enables improved runtime and precision for bio-loggers on seabirds.

Communications biology
Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspe...

Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations.

Environmental research
Reliable estimates of outdoor air pollution concentrations are needed to support global actions to improve public health. We developed a new approach to estimating annual average outdoor nitrogen dioxide (NO) concentrations using approximately 20,000...

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues.

Molecules (Basel, Switzerland)
Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (), are acce...

Presence of emerging organic contaminants and solvents in schools using passive sampling.

The Science of the total environment
In this study, we report on the applicability of passive sampling with Carbopack X adsorbent tubes followed by thermal desorption gas-chromatography-mass spectrometry (TD-GC-MS) to monitor the concentrations of emerging organic contaminants (EOCs) an...

Using machine learning to understand the implications of meteorological conditions for fish kills.

Scientific reports
Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality mon...