AIMC Topic: Environmental Monitoring

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New interpretable deep learning model to monitor real-time PM concentrations from satellite data.

Environment international
Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm (PM) is a key air quality parameter. A real-time knowledge of PM is highly valuable for lowering the risk of detrimental impacts on human health. To achieve th...

Machine Learning-Based Activity Pattern Classification Using Personal PM Exposure Information.

International journal of environmental research and public health
The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM. However, the recording process of an activity pattern can be annoying to study participants, because they are often asked to brin...

Sequence-enabled community-based microbial source tracking in surface waters using machine learning classification: A review.

Journal of microbiological methods
The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the env...

Robotic environmental DNA bio-surveillance of freshwater health.

Scientific reports
Autonomous water sampling technologies may help to overcome the human resource challenges of monitoring biological threats to rivers over long time periods and across large geographic areas. The Monterey Bay Aquarium Research Institute has pioneered ...

A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network.

Ecotoxicology and environmental safety
Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we have developed an efficient air quality warning system based on a low-cost and robust ground-level oz...

Predicting the concentration of total coliforms in treated rural domestic wastewater by multi-soil-layering (MSL) technology using artificial neural networks.

Ecotoxicology and environmental safety
Many indicators are involved in monitoring water quality. For instance, the fecal indicator bacteria are extremely important to detect the water quality. For this purpose, to better predict the total coliforms at the outlet of a Multi-Soil-Layering (...

Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms.

Environmental monitoring and assessment
Drought, which has become one of the most severe environmental problems worldwide, has serious impacts on ecological, economic, and socially sustainable development. The drought monitoring process is essential in the management of drought risks, and ...

Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model.

Sensors (Basel, Switzerland)
The real-time detection of pine cones in Korean pine forests is not only the data basis for the mechanized picking of pine cones, but also one of the important methods for evaluating the yield of Korean pine forests. In recent years, there has been a...

An intelligent way for discerning plastics at the shorelines and the seas.

Environmental science and pollution research international
Irrespective of how plastics litter the coastline or enter the sea, they pose a major threat to birds and marine life alike. In this study, an artificial intelligence tool was used to create an image classifier based on a convolutional neural network...

Multi-sensor information fusion detection system for fire robot through back propagation neural network.

PloS one
OBJECTIVE: To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated.