The defense will take place on Tuesday 6th June at 10 AM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.
Spatio-temporal Data Analysis for Dynamic Phenomenon Monitoring Using Mobile Sensors
Monitoring air pollution in emergencies (industrial accidents, terrorist attacks, volcanic eruptions, etc.) is of utmost importance given the dramatic effects that the released pollutants can cause on both human health and the environment. In 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 a precise mapping of pollution dispersion is needed to mitigate risks.
This thesis focuses on the monitoring of air pollution in emergencies using a fleet of drones, with three main areas of investigation: 1) the spatiotemporal prediction of pollution plume evolution; 2) the optimal planning of drones trajectories to improve pollution mapping; and 3) the development of a generic solution for dynamic pollution monitoring. Through this work, we
propose a spatio-temporal Deep Learning model for multi-point forecasting of pollution concentrations, and we built upon several uncertainty quantification techniques to make it more trustworthy. Furthermore, we examine and identify the main challenges related to the underlying phenomena as well as its emergency context, and we suggest a new systemic approach for monitoring dynamic air pollution based on aerial sensing, that combines Deep Learning approaches, with Data Assimilation techniques, while relying at the same time on adequate path planning
strategies. The framework is then extended to address the data scarcity issues encountered in such situations through a transfer learning solution based on physical models. Finally, we meticulously address the drones’ path planning problem to improve the air pollution mapping quality, and we provide a Multi-Agent Reinforcement Learning solution.
Keywords: Monitoring Dynamic Air Pollution, Spatio-temporal Forecasting, Deep Learning, Multi-Agent Reinforcement Learning, Drones.
- NATALIZIO, Enrico Professeur des universités TII, Abu Dhabi Rapporteur
- MITTON, Natalie Directrice de recherche INRIA Rapportrice
- GARCIA, christophe Professeur des universités INSA-LYON Examinateur
- CARNEIRO Viana, Aline Directrice de recherche INRIA Examinatrice
- LABENTALl, Bérengère Directrice de recherche Université Gustave Eiffel Examinatrice
- RIVANO, hervé Professeur des universités INSA-LYON Directeur de thèse
- BECHKIT, Walid Maître de conférences INSA-LYON Co-directeur de thèse