VigiaEnchente: Static Flood Susceptibility Scoring, Validity Analysis and Risk-Penalized Routing in Urban Road Networks
Resumo
Este estudo avalia grafos em previsões de suscetibilidade a enchentes. A rede viária de Sabará (Brasil) foi modelada com características topológicas, topográficas e fluviais, comparando GCN, uma heurística contínua, MLP (sem arestas) e ablações de features. O heurístico superou os modelos aprendidos (MSE 0.0001; Spearman ρ=1.0000), e o MLP igualou/superou o GCN na correlação, sugerindo que message passing agrega pouco valor com rótulos heurísticos derivados das próprias variáveis de entrada. GCNs foram integrados a roteamento penalizado por risco e a experimento de isolamento topológico, estabelecendo um baseline para futuros estudos de decisão em enchentes.Referências
Agência Nacional de Águas e Saneamento Básico (2025). Estudo aponta que enchentes de 2024 foram o maior desastre natural da história do RS e sugere caminhos para o futuro com eventos extremos mais frequentes. ANA. Acesso em: 7 mar. 2026.
Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R. (2025). Multi-scale hydraulic graph neural networks for flood modelling. Natural Hazards and Earth System Sciences, 25(1):335–351.
Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65:126–139.
Boeing, G. (2022). Street network models and indicators for every urban area in the world. Geographical Analysis, 54(3):519–535.
Brasil (2012). Lei nº 12.651, de 25 de maio de 2012: dispõe sobre a proteção da vegetação nativa (código florestal). Presidência da República. Acesso em: 30 mar. 2026.
Cao, S., Zhou, Y., Tong, Y., Ye, R., Yang, X., Zeng, Y., and Wang, P. (2026). Dynamic evacuation route planning for urban residents in flood disasters. Simulation Modelling Practice and Theory, 147:103250.
Fey, M. and Lenssen, J. E. (2019). Fast graph representation learning with pytorch geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.
Goldbarg, M. and Goldbarg, E. (2012). Grafos: Conceitos, Algoritmos e Aplicações. Elsevier, 1 edition.
Göttlich, S., Kühn, S., Ohst, J. P., and Ruzika, S. (2016). Evacuation modeling: A case study on linear and nonlinear network flow models. EURO Journal on Computational Optimization, 4(3–4):219–239.
He, L., Nan, J., Ye, X., Chen, L., Ji, S., Chen, Z., and Xiao, Q. (2025). A graph neural network using physical attributes to improve the system-wide nodal water-level prediction in sparsely monitored urban drainage systems. Journal of Hydrology, 663:134306.
Kipf, T. N. and Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR).
Liu, T. and Meidani, H. (2026). Graph neural networks for travel distance estimation and route recommendation under probabilistic hazards. International Journal of Transportation Science and Technology, 21:372–383.
O Tempo (2024). Ruas de Sabará, na grande BH, viram ’rios’ e pessoas ficam ilhadas durante temporal. O Tempo. Acesso em: 7 mar. 2026.
Pregnolato, M., Ford, A., Wilkinson, S. M., and Dawson, R. J. (2017). The impact of flooding on road transport: A depth-disruption function. Transportation Research Part D: Transport and Environment, 55:67–81.
Rosa, A. C. (2025). EVACUAI: Planejamento de rotas de fuga adaptativas em ambientes com obstáculos utilizando aprendizado por reforço. Dissertação de mestrado profissional, departamento de engenharia elétrica, Universidade de Brasília, Brasília, DF.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018). Graph attention networks. In International Conference on Learning Representations (ICLR).
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1):4–24.
Zhang, Z., Tian, W., Lu, C., Liao, Z., and Yuan, Z. (2024). Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks. Water Research, 263:122142.
Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., and Li, H. (2020). T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9):3848–3858. Versão arXiv: arXiv:1811.05320.
Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R. (2025). Multi-scale hydraulic graph neural networks for flood modelling. Natural Hazards and Earth System Sciences, 25(1):335–351.
Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65:126–139.
Boeing, G. (2022). Street network models and indicators for every urban area in the world. Geographical Analysis, 54(3):519–535.
Brasil (2012). Lei nº 12.651, de 25 de maio de 2012: dispõe sobre a proteção da vegetação nativa (código florestal). Presidência da República. Acesso em: 30 mar. 2026.
Cao, S., Zhou, Y., Tong, Y., Ye, R., Yang, X., Zeng, Y., and Wang, P. (2026). Dynamic evacuation route planning for urban residents in flood disasters. Simulation Modelling Practice and Theory, 147:103250.
Fey, M. and Lenssen, J. E. (2019). Fast graph representation learning with pytorch geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.
Goldbarg, M. and Goldbarg, E. (2012). Grafos: Conceitos, Algoritmos e Aplicações. Elsevier, 1 edition.
Göttlich, S., Kühn, S., Ohst, J. P., and Ruzika, S. (2016). Evacuation modeling: A case study on linear and nonlinear network flow models. EURO Journal on Computational Optimization, 4(3–4):219–239.
He, L., Nan, J., Ye, X., Chen, L., Ji, S., Chen, Z., and Xiao, Q. (2025). A graph neural network using physical attributes to improve the system-wide nodal water-level prediction in sparsely monitored urban drainage systems. Journal of Hydrology, 663:134306.
Kipf, T. N. and Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR).
Liu, T. and Meidani, H. (2026). Graph neural networks for travel distance estimation and route recommendation under probabilistic hazards. International Journal of Transportation Science and Technology, 21:372–383.
O Tempo (2024). Ruas de Sabará, na grande BH, viram ’rios’ e pessoas ficam ilhadas durante temporal. O Tempo. Acesso em: 7 mar. 2026.
Pregnolato, M., Ford, A., Wilkinson, S. M., and Dawson, R. J. (2017). The impact of flooding on road transport: A depth-disruption function. Transportation Research Part D: Transport and Environment, 55:67–81.
Rosa, A. C. (2025). EVACUAI: Planejamento de rotas de fuga adaptativas em ambientes com obstáculos utilizando aprendizado por reforço. Dissertação de mestrado profissional, departamento de engenharia elétrica, Universidade de Brasília, Brasília, DF.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018). Graph attention networks. In International Conference on Learning Representations (ICLR).
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1):4–24.
Zhang, Z., Tian, W., Lu, C., Liao, Z., and Yuan, Z. (2024). Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks. Water Research, 263:122142.
Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., and Li, H. (2020). T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9):3848–3858. Versão arXiv: arXiv:1811.05320.
Publicado
19/10/2026
Como Citar
RODRIGUES, Raphael A.; XAVIER, Pedro H. S. F.; MACEDO, Paulo M. C.; SOARES, Lívia M.; S. JUNIOR, Carlos A.; SILVA, Carlos A..
VigiaEnchente: Static Flood Susceptibility Scoring, Validity Analysis and Risk-Penalized Routing in Urban Road Networks. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 3238-3249.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31278.
