AIMC Topic: Atmosphere

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Deep learning bias correction of GEMS tropospheric NO: A comparative validation of NO from GEMS and TROPOMI using Pandora observations.

Environment international Jun 13, 2024
Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric colum...
Deep Learning Environmental Monitoring Atmosphere Nitrogen Dioxide Bias Air Pollutants
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Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations.

The Science of the total environment Aug 23, 2022
Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is e...
Allergens Atmosphere Neural Networks, Computer Reproducibility of Results Pollen Poaceae
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A neural network based approach to classify VLF signals as rock rupture precursors.

Scientific reports Aug 12, 2022
The advent of novel technologies revealed that other geophysical signals than those directly related to fault motion could be used to probe the state of deformation of the Earth's crust. Electromagnetic signals belonging to this category have been in...
Atmosphere Neural Networks, Computer Earthquakes
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Generating a long-term (2003-2020) hourly 0.25° global PM dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS).

The Science of the total environment Jul 31, 2022
Generating a long-term high-spatiotemporal resolution global PM dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Coper...
Deep Learning Air Pollution Particulate Matter Environmental Monitoring Air Pollutants Atmosphere
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Design of experiments meets immersive environment: Optimising eating atmosphere using artificial neural network.

Appetite Jun 5, 2022
Design of experiments (DOE) is a family of statistical tools commonly used in food science to optimise recipes and facilitate new food development. In a novel cross-disciplinary twist, we propose to adapt DOE approach to the optimisation of restauran...
Atmosphere Reproducibility of Results Food Humans Neural Networks, Computer Algorithms
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Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data.

Scientific reports May 19, 2022
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised mach...
Atmosphere Forecasting Weather Neural Networks, Computer Machine Learning
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A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration.

Environmental science and pollution research international May 3, 2021
Accuracy in the prediction of the particulate matter (PM and PM) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM and P...
Neural Networks, Computer Particulate Matter Environmental Monitoring Atmosphere
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A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network.

Ecotoxicology and environmental safety Aug 22, 2020
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...
Environmental Monitoring Taiwan Air Pollutants Air Pollution Algorithms Cities Neural Networks, Computer Seasons Atmosphere Ozone Least-Squares Analysis
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Forecasting of bioaerosol concentration by a Back Propagation neural network model.

The Science of the total environment Sep 5, 2019
Bioaerosol in the atmosphere plays a very important role in environment and public health. To forecast the bioaerosol concentration, the correlation between bioaerosol concentration and meteorological factors was discussed, and a Back Propagation (BP...
Meteorological Concepts Aerosols Neural Networks, Computer Environmental Monitoring Atmosphere Air Pollution
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Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Environmental monitoring and assessment Jun 7, 2019
Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen de...
Oklahoma Radar Pollen Weather Plant Extracts Antigens, Plant Machine Learning Allergens Environmental Monitoring Atmosphere Forecasting
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