The National Institute of Advanced Studies (NIAS), Bengaluru, under an ISRO chair professor, has launched a pilot project merging Geospatial Artificial Intelligence (GeoAI) and random forest technology to monitor and predict urban air quality. GeoAI combines AI with geospatial data, accelerating insights into environmental impacts and operational risks. Using smartphone apps, real-time feedback on conditions like traffic congestion is collected, enhancing accuracy. GeoAI streamlines data workflows, speeds situational awareness, and aids decision-making through spatial patterns. The project employs the random forest algorithm, leveraging historical air quality data to predict the Air Quality Index, addressing urban air quality challenges effectively.
Facts/Terms for UPSC Prelims
- Geospatial Data: Information linked to geographic coordinates, allowing for spatial analysis. GeoAI combines AI techniques with such data to gain insights into various phenomena, as showcased in the air quality prediction project.
- Air Quality Index (AQI): A numerical value representing the quality of the air in a specific area. The GeoAI project utilizes historical data and random forest technology to forecast AQI levels and potential environmental impacts.
- Machine Learning Algorithm: Random Forest: An algorithm that aggregates outputs from multiple data inputs to reach a result. In the air quality prediction context, this technology utilizes historical data from various monitoring stations to forecast air quality levels.
