Fine-grained air quality exposure modeling and forecasting using machine learning.
Implementing Organization
IIT Gandhinagar ,Gujarat & IIT Delhi
Principal Investigator
PI: Dr. Nipun Batra
Indian Institute of Technology Gandhinagar
Computer Science and Engineering
nipun.batra@iitgn.ac.in
CO-Principal Investigator
Dr. SagnikDey
Indian Institute of (IIT)Technology Delhi
Centre for Atmospheric Sciences
riju@cse.iitd.ac.in,sagnik@cas.iitd.ac.in
CO-Principal Investigator
Dr. RijurekhaSen
Indian Institute of (IIT)Technology Delhi
riju@cse.iitd.ac.in,sagnik@cas.iitd.ac.in
CO-Principal Investigator
Dr. Udit Bhatia
Indian Institute of Technology Gandhinagar
Discipline of Civil Engineering
riju@cse.iitd.ac.in,sagnik@cas.iitd.ac.in
Project Overview
Development of novel machine learning algorithms for leveraging various grades of air quality sensors (with different noise assumptions) and accurately inferring a spatially fine-grained air quality map of a megacity is attempted. Implementation of novel fusion techniques to leverage multi-modal data from sources including, but not limited to: satellite retrievals, traffic data, high-resolution (spatial and temporal) meteorological data, population density, geophysical variables (e.g. distance to road, land use pattern, non-vehicle point sources, etc. Implementation of data-driven air quality forecasting models leveraging various meteorological data, and advances in machine learning
IIT Gandhinagar ,Gujarat & IIT Delhi
Computer Science and Engineering
nipun.batra@iitgn.ac.in
Acces sibility Controls