Near real-time air quality forecasts using the NASA GEOS model

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

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Machine Learning Techniques for Air Quality Forecasting and Study on Real-Time Air Quality Monitoring

2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) ◽

10.1109/iccubea.2017.8463746 ◽

2017 ◽

Cited By ~ 1

Author(s):

Varsha Hable-Khandekar ◽

Pravin Srinath

Keyword(s):

Machine Learning ◽

Air Quality ◽

Real Time ◽

Quality Monitoring ◽

Machine Learning Techniques ◽

Air Quality Monitoring ◽

Learning Techniques ◽

Air Quality Forecasting

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Near real-time twitter spam detection with machine learning techniques

International Journal of Computers and Applications ◽

10.1080/1206212x.2020.1751387 ◽

2020 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Nan Sun ◽

Guanjun Lin ◽

Junyang Qiu ◽

Paul Rimba

Keyword(s):

Machine Learning ◽

Real Time ◽

Machine Learning Techniques ◽

Spam Detection ◽

Learning Techniques

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Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

Journal Of Big Data ◽

10.1186/s40537-021-00539-2 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Majid Amirfakhrian ◽

Mahboub Parhizkar

Keyword(s):

Machine Learning ◽

Image Processing ◽

Machine Vision ◽

Traffic Accidents ◽

Fuzzy Theory ◽

Machine Learning Techniques ◽

Industrial Setting ◽

Technological Advances ◽

Learning Techniques ◽

Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

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Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2020.372 ◽

2020 ◽

pp. 1-8

Author(s):

Jasleen Kaur Sethi ◽

Mamta Mittal

Keyword(s):

Machine Learning ◽

Air Quality ◽

Air Pollutants ◽

Machine Learning Techniques ◽

Environmental Restoration ◽

The Novel ◽

Ozone Pollution ◽

Learning Techniques ◽

Novel Coronavirus ◽

The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

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