A Decision-support Service for Firefighting in Environments of Dry Tropical Forest
Resumo
Forest fires significantly threaten the environment, society, and economy by harming biodiversity, causing economic losses, displacing communities, and impacting air and water quality. This article presents an innovative real-time monitoring application for forest fires, enhancing early detection, rapid response, and efficient coordination for Brazilian fire brigades. The app offers features such as fire location tracking, weather updates, image and video storage, and fire outbreak management. It aggregates data from various sources to provide valuable information for strategic planning and operational support.
Palavras-chave:
Mobile Application, Business Intelligence, Forest Fire, Dry Tropical Forest
Referências
Arab, S. T., Islam, M. M., Shamsuzzoha, M., Alam, K. F., Muhsin, N., Noguchi, R., and Ahamed, T. (2022). A review of remote sensing applications in agriculture and forestry to establish big data analytics. Remote Sensing Application: Regional Perspectives in Agriculture and Forestry, pages 1–24.
Castro-Basurto, K., Jijon-Veliz, F., Medina, W., and Velasquez, W. (2021). Outside dynamic evacuation routes to escape a wildfire: A prototype app for forest firefighters. Sustainability, 13(13):7295.
Cazzolato, M. T., Avalhais, L. P., Chino, D. Y., Ramos, J. S., de Souza, J. A., Rodrigues-Jr, J. F., and Traina, A. (2017). Fismo: A compilation of datasets from emergency situations for fire and smoke analysis. In Brazilian symposium on databases-SBBD, pages 213–223. SBC Uberlândia, Brazil.
Çolak, E. and Sunar, F. (2020). The importance of ground-truth and crowdsourcing data for the statistical and spatial analyses of the nasa firms active fires in the mediterranean turkish forests. Remote Sensing Applications: Society and Environment, 19:100327.
da Silva, S. S., Fearnside, P. M., de Alencastro Graça, P. M. L., Brown, I. F., Alencar, A., and de Melo, A. W. F. (2018). Dynamics of forest fires in the southwestern amazon. Forest Ecology and Management, 424:312–322.
Komalapati, N., Yarra, V. C., Kancharla, L. A. V., and Shankar, T. (2021). Smart fire detection and surveillance system using iot. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pages 1386–1390. IEEE.
Maia, J. M., de Oliveira Sousa, V. F., de Lira, E. H. A., and de Lucena, A. M. A. (2017). Motivações socioeconômicas para a conservação e exploração sustentável do bioma caatinga. Desenvolvimento e meio ambiente, 41.
Seifert, M., Kuehnel, S., and Sackmann, S. (2023). Hybrid clouds arising from software as a service adoption: challenges, solutions, and future research directions. ACM Computing Surveys, 55(11):1–35.
Shah, S. A., Seker, D. Z., Hameed, S., and Draheim, D. (2019). The rising role of big data analytics and iot in disaster management: recent advances, taxonomy and prospects. IEEE Access, 7:54595–54614.
Silva Junior, J. A. d. and Pacheco, A. d. P. (2021). Avaliação de incêndio em ambiente de caatinga a partir de imagens landsat-8, índice de vegetação realçado e análise por componentes principais. Ciência Florestal, 31:417–439.
Subburaj, J., Murugan, K., Keerthana, P., and Aalam, S. S. (2024). Catastropheguard: A guard against natural catastrophes through advances in ai and deep learning technologies. In Internet of Things and AI for Natural Disaster Management and Prediction, pages 28–55. IGI Global.
Castro-Basurto, K., Jijon-Veliz, F., Medina, W., and Velasquez, W. (2021). Outside dynamic evacuation routes to escape a wildfire: A prototype app for forest firefighters. Sustainability, 13(13):7295.
Cazzolato, M. T., Avalhais, L. P., Chino, D. Y., Ramos, J. S., de Souza, J. A., Rodrigues-Jr, J. F., and Traina, A. (2017). Fismo: A compilation of datasets from emergency situations for fire and smoke analysis. In Brazilian symposium on databases-SBBD, pages 213–223. SBC Uberlândia, Brazil.
Çolak, E. and Sunar, F. (2020). The importance of ground-truth and crowdsourcing data for the statistical and spatial analyses of the nasa firms active fires in the mediterranean turkish forests. Remote Sensing Applications: Society and Environment, 19:100327.
da Silva, S. S., Fearnside, P. M., de Alencastro Graça, P. M. L., Brown, I. F., Alencar, A., and de Melo, A. W. F. (2018). Dynamics of forest fires in the southwestern amazon. Forest Ecology and Management, 424:312–322.
Komalapati, N., Yarra, V. C., Kancharla, L. A. V., and Shankar, T. (2021). Smart fire detection and surveillance system using iot. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pages 1386–1390. IEEE.
Maia, J. M., de Oliveira Sousa, V. F., de Lira, E. H. A., and de Lucena, A. M. A. (2017). Motivações socioeconômicas para a conservação e exploração sustentável do bioma caatinga. Desenvolvimento e meio ambiente, 41.
Seifert, M., Kuehnel, S., and Sackmann, S. (2023). Hybrid clouds arising from software as a service adoption: challenges, solutions, and future research directions. ACM Computing Surveys, 55(11):1–35.
Shah, S. A., Seker, D. Z., Hameed, S., and Draheim, D. (2019). The rising role of big data analytics and iot in disaster management: recent advances, taxonomy and prospects. IEEE Access, 7:54595–54614.
Silva Junior, J. A. d. and Pacheco, A. d. P. (2021). Avaliação de incêndio em ambiente de caatinga a partir de imagens landsat-8, índice de vegetação realçado e análise por componentes principais. Ciência Florestal, 31:417–439.
Subburaj, J., Murugan, K., Keerthana, P., and Aalam, S. S. (2024). Catastropheguard: A guard against natural catastrophes through advances in ai and deep learning technologies. In Internet of Things and AI for Natural Disaster Management and Prediction, pages 28–55. IGI Global.
Publicado
14/10/2024
Como Citar
ARAÚJO, Tiago Brasileiro et al.
A Decision-support Service for Firefighting in Environments of Dry Tropical Forest. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 820-826.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.243628.
