A generic framework for monitoring pollution plumes in emergencies using UAVs
| Autor: | Ichrak Mokhtari, Walid Bechkit, Hervé Rivano |
|---|---|
| Přispěvatelé: | ALGorithmes et Optimisation pour Réseaux Autonomes (AGORA), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), AlGorithmes et Optimisation pour Réseaux Autonomes (AGORA), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Lyon |
| Rok vydání: | 2021 |
| Předmět: |
Pollution
Dynamic air pollution Computer science media_common.quotation_subject Real-time computing Air pollution 02 engineering and technology UAVs medicine.disease_cause Data Assimilation [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Deep Learning Data assimilation 11. Sustainability 0202 electrical engineering electronic engineering information engineering medicine [INFO]Computer Science [cs] Air quality index media_common Pollutant Uncertainty Drone Plume 13. Climate action Trajectory 020201 artificial intelligence & image processing |
| Zdroj: | IJCNN International joint conference on neural networks (IJCNN) International joint conference on neural networks (IJCNN), Jul 2021, Online, United States IJCNN 2021-International Joint Conference on Neural Networks IJCNN 2021-International Joint Conference on Neural Networks, Jul 2021, Online, United States. pp.1-9 IJCNN 2021-International Joint Conference on Neural Networks, Jul 2021, Online, United States. pp.1-9, ⟨10.1109/IJCNN52387.2021.9533896⟩ |
| DOI: | 10.1109/ijcnn52387.2021.9533896 |
| Popis: | International audience; Monitoring air pollution plumes in emergency situations (industrial accidents, natural disasters, deliberate terrorist releases, etc.) becomes an issue of utmost importance in our society given the dramatic effects that the released pollutants can cause. Considering these situations, the pollution plume is strongly dynamic leading to a fast dispersion of pollutants in the atmosphere. Thus, the need for real-time response is very strong and a solution to get precise mapping of pollution dispersion is required to mitigate risks. However, monitoring and forecasting air quality in real time in such situations remains a highly challenging endeavour. In this paper, we suggest a systemic approach for monitoring dynamic air pollution based on aerial sensing (sensors mounted on UAVs). The proposed framework consists of a cycle with feedback loop which will constantly combine a spatio-temporal forecasting model based on a convolutional long short term memory (ConvLSTM) network with a data assimilation technique to get accurate pollution maps, while adjusting at each time the trajectories of drones following uncertainty forecasts. Our solution was evaluated and validated using a highly dynamic real world data set namely Fusion Field Trial 2007 (FFT07). The proposed strategy, together with the obtained evaluation results, are presented, and carefully analyzed. |
| Databáze: | OpenAIRE |
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