Abstract
Wildfires are a growing threat as they can lead to significant casualties and result in damages to the economy and environment. Although risk mitigation and prior preparation are important, some wildfire causes make the disaster difficult to predict and therefore to prevent, hence the importance of improving disaster response capabilities. In this paper, we tackle the problem of determining the entry and exit points for firefighting Unmanned Aerial Vehicles (UAVs) when approaching and leaving the wildfire zone. The entry and exit point are scored based on the time the UAVs spend in the fire zone and the time to reach the fire zone. The problem is formulated as a regression model, which is tackled using machine learning algorithms, namely decision trees and random forest. The methods are simulated and evaluated on synthetic data, and the results show that the approach was able to provide accurate rankings of the entry and exit points.
Original language | English |
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Title of host publication | 6th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI-2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Publication date | 2022 |
ISBN (Electronic) | 9781665476072 |
DOIs | |
Publication status | Published - 2022 |
Event | 6th SLAAI International Conference on Artificial Intelligence, SLAAI-ICAI-2022 - Virtual, Online, Sri Lanka Duration: 1 Dec 2022 → 2 Dec 2022 |
Conference
Conference | 6th SLAAI International Conference on Artificial Intelligence, SLAAI-ICAI-2022 |
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Country/Territory | Sri Lanka |
City | Virtual, Online |
Period | 01/12/2022 → 02/12/2022 |
Series | 6th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI-2022 |
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Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Fast disaster response
- UAV-assisted wildfire mission planning