AIMC Topic: Weather

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PM concentration estimation using convolutional neural network and gradient boosting machine.

Journal of environmental sciences (China)
Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM) concentration. Here we explo...

Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs.

Risk analysis : an official publication of the Society for Risk Analysis
Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are...

Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data.

Accident; analysis and prevention
Providing drivers with real-time weather information and driving assistance during adverse weather, including fog, is crucial for safe driving. The primary focus of this study was to develop an affordable in-vehicle fog detection method, which will p...

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

Accident; analysis and prevention
Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks...

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning.

Computational intelligence and neuroscience
We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weat...

Transductive LSTM for time-series prediction: An application to weather forecasting.

Neural networks : the official journal of the International Neural Network Society
Long Short-Term Memory (LSTM) has shown significant performance on many real-world applications due to its ability to capture long-term dependencies. In this paper, we utilize LSTM to obtain a data-driven forecasting model for an application of weath...

Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.

Accident; analysis and prevention
Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data compri...

Deep learning for pollen allergy surveillance from twitter in Australia.

BMC medical informatics and decision making
BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data colle...

Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.

Environmental monitoring and assessment
Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in Nort...

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Environmental monitoring and assessment
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...