Abstract
Wildfires pose a significant threat to the environment and local communities, and predicting their occurrence is crucial for effective management and prevention. The Caribbean region is particularly susceptible to wildfires due to factors such as human activities, climate change, and natural causes. In this article, we propose a comprehensive methodology that combines data from multi-source satellite data and applies a range of predictive models. The results demonstrate the potential of deep learning techniques for identifying high-risk areas and developing effective fire management strategies. They also highlight the importance of continued research and investment in this area to improve the accuracy of predictive models and ultimately ensure the safety of communities and the environment. The findings have important implications for policymakers and stakeholders in the Caribbean region, who can use this information to develop more effective fire management strategies to minimize the impact of wildfires on the environment and local communities. By identifying high-risk areas, preventative measures such as controlled burns and improved fire management strategies can be implemented.
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Acknowledgements
The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the projects PID2020-117954RB and TED2021-131311B, and the European Regional Development Fund and Junta de Andalucía for projects PY20-00870 and UPO-138516. This work has also been funded by the Becas Iberoamérica: Santander Investigación 2021.
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Torres, J.F., Valencia, S., Martínez-Álvarez, F., Hoyos, N. (2023). Predicting Wildfires in the Caribbean Using Multi-source Satellite Data and Deep Learning. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_1
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