Using Machine Learning & Deep Learning to Improve Trace Gas and Particulate Matter (PM) Estimations
Dr. Alqamah Sayeed presented at the SERVIR's Geo-AI Working Group on May 29, 2024.
Dr. Alqamah Sayeed is an accomplished atmospheric scientist with a background in Machine Learning (ML), Deep Learning (DL), and numerical modeling for weather and air quality forecasting. He currently serves as an Associate Scientist at the Universities Space Research Association (USRA) in Huntsville, Alabama, where he applies advanced computational techniques to improve the estimation of trace gases and air pollutants using satellite and numerical modeling data. He has been instrumental in supporting air quality and health impact studies across regional hubs in Southeast Asia, the Hindu Kush, and East and South Africa for the NASA-SERVIR program.
In this talk, Alqamah presented three projects that he is working/worked on: (1) Downscaling Air Quality Forecast from GEOS-FP over South-east Asia, (2) Integrating Satellite-derived PM2.5 into EPA's AirNow System, and (3) CNN-based Model for Spatial Imputation (CMSI Version 1.0) of In-situ ozone and PM2.5 measurements. All these study shows the advance uses of Machine Learning approaches to the Air Quality applications.
Find Alqamah's presentation here: https://docs.google.com/presentation/...
View the SERVIR-SEA Air Quality Explorer here: https://aqatmekong-servir.adpc.net/en...
Find relevant papers here:
https://www.sciencedirect.com/science...
https://www.nature.com/articles/s4159...
https://www.sciencedirect.com/science...
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