A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements
Abstract
:1. Introduction
- The model should be a spatiotemporal model, and therefore, be capable of predicting changes in chemical concentrations in both space and time.
- The model should also be a control-theoretic or parametric model since it is required for our model-based controller.
- The model should require limited data for learning since it is very difficult to obtain wildfire chemical data to learn a data-driven model.
- Finally, the model should be computationally inexpensive since it must be computed onboard the UAVs using limited computational resources.
2. Materials and Methods
2.1. UAV Platform and Air Quality Sensor Package
2.2. Chemical Data Collection
- (1)
- Use one mobile sensing robot where the robot must stop at each waypoint for more than one time step to collect data at that point.
- (2)
- Use one mobile sensing robot and one static reference sensor where the mobile sensor can continuously move while collecting data and the static sensor continuously collects data at a specific location within the field being sampled. For this to work, the static sensor must be the same as the mobile sensor and both must be synchronized in time.
- (3)
- Use multiple mobile robots where all robots start at different waypoints but follow the same trajectory such that if one robot collects data at a specific waypoint at the current time step, another robot will collect measurements at that waypoint in the future time step.
2.3. Spatio-Temporal Modelling
2.4. Learning the Spatiotemporal Model from Data
2.5. Comparison with Existing Model
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
UAV | Unmanned Aerial Vehicle |
VOC | Volatile Organic Compound |
SID | Subspace Identification |
GPM | Gaussian Puff Model |
MAE | Mean Absolute Error |
RMSE | Root Mean Squared Error |
ppm | Parts Per Million |
CAL FIRE | California Department of Forestry and Fire Protection |
CARB | California Air Resources Board |
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Sensor | Measurement | Operating Range |
---|---|---|
Temperature | −40 to 85 °C | |
BME680 | Barometric Pressure | 300 to 1100 hPa |
Relative Humidity | 0 to 100% r.H. | |
SGP30 | eCO2 | 400–60,000 ppm |
Total VOC | 0–60,000 ppb | |
SCD41 | CO2 | 400–5000 ppm |
PMSA003I | Particulate Matter | 0–500 g/m3 |
SEN0231 | Formaldehyde (HCHO) | 0–5 ppm |
CO | 1–1000 ppm | |
MiCS-4514 | NO2 | 0.05–10 ppm |
NH3 | 1–500 ppm |
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Ragbir, P.; Kaduwela, A.; Lan, X.; Watts, A.; Kong, Z. A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements. Drones 2024, 8, 169. https://doi.org/10.3390/drones8050169
Ragbir P, Kaduwela A, Lan X, Watts A, Kong Z. A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements. Drones. 2024; 8(5):169. https://doi.org/10.3390/drones8050169
Chicago/Turabian StyleRagbir, Prabhash, Ajith Kaduwela, Xiaodong Lan, Adam Watts, and Zhaodan Kong. 2024. "A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements" Drones 8, no. 5: 169. https://doi.org/10.3390/drones8050169