Hybrid Computational Model to Assist in the Location of Victims Buried in the Tragedy of Brumadinho

Authors

  • Rafael Fernandes Pinheiro UNIVERSIDADE DE SÃO PAULO
  • Harold Ivan Angulo Bustos
  • Bruno Bestle Turrin
  • Francisco Marcos da Costa Monteiro
  • Diego Colón
  • Mirelly Ferreira Silveira
  • Dário José Aloise
  • Alysson Mendes de Oliveira

DOI:

https://doi.org/10.24221/jeap.6.3.2021.4103.183-193

Abstract

This paper presents a hybrid computational model  based on regression techniques, machine learning and  physicomathematical algorithms developed for assistance in locating victims in the Brumadinho tragedy in 2019. The physicomathematical model, which provided results to help search teams, is based on integral and vector calculus, and fluid mechanics concepts. In addition, from data provided by the physicomathematical algorithm, two hybrid model were developed. One of them uses regression statistical and the  other one uses support vector regression which is a type of machine learning. With good prospects of the advances in research, it is  expected in future work, a more accurate model that can be used in other possible situations of dam-break. Moreover the model can be applied to situations involving computational fluid dynamics in general

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References

Alvarado, L. A. S. 2006. Two-dimensional simulation of debris runs using the Discrete Element Method (in Portuguese). Master's thesis. PUC-Rio. Rio de Janeiro, Brasil. 154p.

Weter, D.; Galvão, D. 2019. Brumadinho Dam has a volume of 12.7 million m³ of mining rejects. (in Portuguese). Cotidiano-Uol Notícias. Available at: https://noticias.uol.com.br/ultimas-noticias/agencia-estado/2019/01/25/barragem-em-brumadinho-tem-volume-de-1-milhao-de-m-de-rejeito-de-mineracao.htm. Access on: 31 August 2021.

Boser, B. E.; Guyon I. M.; Vapnik, V. N. 1992. A Training Algorithm for Optimal Margin Classifiers. In: D. Haussler (Ed.), COLT '92: Proceedings of the fifth annual workshop on Computational. New York: ACM Press. pp. 144-152.

Burden, R. L.; Faires, J. D.; Burden., A. M. 2015. Numerical Analysis. Cengage Learning. 912p.

Burges, C. J. 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121-167.

Daneshvar, P.; Zsaki, A. M. 2018. Simulation of tailings flow resulting from a dam breach using smoothed particle hydrodynamics. Environmental and Engineering Geoscience, 24, (3), 263-279.

Gomes., H. S. 2020. From mud to patent: scientists made Brumadinho's tragedy turn into innovation. (in Portuguese). Tilt-Uol Notícias. Available at: https://www.uol.com.br/tilt/noticias/redacao/2020/02/12/morte-na-lama-como-cientistas-inovaram-apos-tragedia-de-brumadinho.htm. Access on: 29 September 2020.

Guzman, S. M.; Paz, J. O.; Tagert, M. L. M. et al. 2019. Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines. Environ Model Assess, 24, 223-234.

Li, X.; Luo, A.; Li, J. et al. 2019. Air Pollutant Concentration Forecast Based on Support Vector Regression and Quantum-Behaved Particle Swarm Optimization. Environ Model Assess, 24, 205-222.

Luo, C.; Xu, K.; Zhao, Y. 2017. A TVD discretization method for shallow water equations: Numerical simulations of tailing dam break. International Journal of Modeling, Simulation, and Scientific Computing, 08, (03), 1850001.

Marcolino, D. A.; Medina, A.; Rafael, C. M.; Amner, R. 2018. Periodic Steady State Assessment of Microgrids with Photovoltaic Generation Using Limit Cycle Extrapolation and Cubic Splines. Energies, 11, (8), 2096.

Moazenzadeh, R.; Mohammadi, B.; Shamshirband, S.; Chau, K. 2018. Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Engineering Applications of Computational Fluid Mechanics, 12, (1), 584-597.

Muson, B. R.; Young, D. F.; Okiishi, T. H. 2004. Fundamentos da Mecânica dos Fluidos. Edgar Blüsher. 584p.

Piovezan., S. 2019. How network of scientists uses data to try to find bodies in Brumadinho. (in Portuguese). Uol Notícias. Available at: https://www.uol.com.br/tilt/noticias/redacao/2019/03/08/cientistas-criam-rede-para-ajudar-vitimas-de-brumadinho-e-evitar-tragedias.htm. Access on: 29 September 2020.

Qasem, S. N.; Samadianfard, S.; Kheshtgar, S. et al. 2019. Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13, (1), 177-187.

Rehamnia, I.; Benlaoukli, B.; Heddam, S. 2020. Modeling of Seepage Flow Through Concrete Face Rockfill and Embankmen Dams Using Three Heuristic Artificial Intelligence Approaches: A Comparative Study. Environ. Process, 7, 367-381.

Santamaría-Bonfil, G.; Reyes-Ballesteros, A.; Gershenson., C. 2016. Wind speed forecasting for wind farms: A method based on support vector regression. Renewable Energy, 85, 790-809.

Seyedashraf, O.; Mehrabi, M.; Akhtari, A. A. 2018. Novel approach for dam break flow modeling using computational intelligence. Journal of Hydrology, Vol. 559.

Tabari, M. M. R.; Sanayei, H. R. Z. 2019. Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models. Soft Comput, 23, 9629-9645.

Vacondio, R.; Palù, A. D.; Mignosa, P. 2014 GPU-enhanced Finite Volume Shallow Water solver for fast flood simulations. Environmental Modelling & Software, 57, 60-75.

Zhang, M., Xu, Y.; Qiao, H. 2018. Numerical Study of Hydrodynamic and Solute Transport with Discontinuous Flows in Coastal Water. Environ Model Assess, 23, 353-367.

Zhou, J.; Causon, D.; Mingham, C.; Ingram., D. 2004. Numerical Prediction of Dam Break Flows in General Geometries with Complex Bed Topography. Journal of Hydraulic Engireering, 130, (4), 332-340.

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Published

2021-09-06

How to Cite

Pinheiro, R. F., Bustos, H. . I. A. ., Turrin, B. B. ., Monteiro, F. M. da C., Colón, D., Silveira, M. F., Aloise, D. J. ., & Oliveira, A. M. de . (2021). Hybrid Computational Model to Assist in the Location of Victims Buried in the Tragedy of Brumadinho. Journal of Environmental Analysis and Progress, 6(3), 183–193. https://doi.org/10.24221/jeap.6.3.2021.4103.183-193