Early-warning systems built on an AI-powered, IoT-driven multi-gas emission sensor platform
The global energy transition is forcing the movement away from fossil fuels. There is a significant need to develop low-cost monitoring solutions that provide early-warning for trigger events. This paper will provide examples of early-warning systems that have been developed. Results from the AI-powered, IoT-driven system built on the multi-gas emission sensor platform will be presented.
Authors
- Sleight Halley (ORCID: https://orcid.org/0000-0003-2832-9433)
- Robert Ian
- Kamil Agi (ORCID: https://orcid.org/0000-0002-5939-8063)
- Ramiro Jordán (ORCID: https://orcid.org/0000-0001-5634-6236)
- Lok‐kun Tsui (ORCID: https://orcid.org/0000-0001-7381-0686)
- Fernando H. Garzón (ORCID: https://orcid.org/0000-0002-4511-9932)
- James A Smith (ORCID: https://orcid.org/0000-0002-5565-9254)
- Tony Quinones
- Donna Koechner
Institutions
- University of New Mexico (US)
Publication Details
- Published
- 2024-08-11
- DOI
- https://doi.org/10.1109/mwscas60917.2024.10658655
- Citations
- 1
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.21