MULTI-OBJECTIVE CALIBRATION OF IBIS MODEL BY GENETIC ALGORITHM WITH PARAMETRIC SENSITIVITY ANALYSIS

Autores

  • Amarísio da Silva Araújo Universidade Federal de Viçosa
  • Haroldo de Campos Velho Instituto Nacional de Pesquisas Espaciais (INPE)
  • Lu Minjiao Nagaoka University of Technology

DOI:

https://doi.org/10.5902/2179460X20095

Palavras-chave:

Multi-objective calibration. IBIS model. Morris’ method. NSGA-II. FloNa Tapajós.

Resumo

Atmospheric circulation models combine different modules for a good description of the atmospheric dynamics. One of these modules is the
representation of surface coverage, since the dynamics depends on the interaction between the atmosphere and the surface of the planet.
However, these modules depend on a number of parameters that need to be adjusted. The parameter adjustment process is called model
calibration. In this study, the IBIS (Integrated Biosphere Simulator) model is calibrated following a multi-objective strategy. The Pareto set,
which embraces the non-dominated solutions in the search space of objective functions, is determined by a version of multi-objective genetic
algorithm (NSGA-II). The model sensitivity to the parameters is evaluated by the Morris’ method. Synthetic data for calibration were
obtained from the Tapajós National Forest (FloNa Tapajós), located near to the 67 km from Santarém-Cuiabá highway (2,51S, 54,58W).

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Biografia do Autor

Amarísio da Silva Araújo, Universidade Federal de Viçosa

Professor Adjunto do Departamento de Matemática da Universidade Federal de Viçosa. Doutor em Computação Aplicada pelo Instituto Nacional de Pesquisas Espaciais, tendo desenvolvido a tese intitulada: "Calibração multiobjetivo de modelos hidrológico e de superfície atmosférico".

Haroldo de Campos Velho, Instituto Nacional de Pesquisas Espaciais (INPE)

Pesquisador titular do Instituto Nacional de Pesquisas Espaciais. Tem experiência na área de Matemática Aplicada e Computação Científica, atuando principalmente nos seguintes temas: problemas inversos, assimilação de dados, modelos de turbulência para atmosfera, redes neurais artificiais, métodos numéricos.

Lu Minjiao, Nagaoka University of Technology

Professor no Departamento de Engenharia Civil e Ambiental da Nagaoka University of Technology. Trabalha em modelos de hidrologia em hidrologia; vem desenvolvendo um sistema de de modelagem hidrológica distribuída para simular o movimento da água sobre a superfície da terra.

Referências

Araújo, A. S., Campos Velho, H. F., Gomes, V. C. F. (2013): Calibrating an hydrological model by an evolutionary strategy for multi-objective optimization. Inverse Problems in Science & Engineering, 21, 438-450.

Araújo, A. S. (2014): Multi-objective calibration of hydrologic and atmospheric surface models. Ph.D. thesis on Applied Computing. São José dos Campos (SP), Brazil (in Portuguese).

Beck, J. V., Blackwell, B., Clair Jr., C. R. St. (1985): Inverse Heat Conduction: Ill-Posed Problems. Wiley-Interscience.

Coello, C. A., Lamont, G. B., Van Veldhuisen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Springer.

Hwang, C-L, Masud, A. S. M. (1979): Multiple objective decision making, methods and applications: a state-of-the-art survey. Springer-Verlag.

Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2000): A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.

Deb, K. (2009): Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.

Foley, J. Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., Haxeltine, A. (1996): An integrated biosphere model of land surfaceprocesses, terrestrial carbon balance, and vegetation dynamics. Global Biogeochemical Cycles, 10(4), 603–628.

Kucharik, C. J., Foley, J. A., Delire, C., Fischer, V. A., Coe, M. T., Lenters, J. D., Young-Molling, C., Ramankutta, N., Norman, J. M., Gower, S. T. (2000): Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure. Global Biogeochem. Cycles, 14(3), 795–825.

Miettinen, K. (1999). Nonlinear Multiobjective Optimization. Springer.

Minjiao, L., Xiao, L. (2014): Time scale dependent sensitivities of the XinAnJiang model parameters. Hydrological Research Letters, 8(1), 51–56.

Morris, M. D. (1991): Factorial sampling plans for preliminary computational experiments. Technometrics (American Society for Quality Control and American Statistical Association, 33(2), 161–174.

Srinivas, N., Deb, K. (1994): Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.

Varejão, C. M., Costa, M. H., Camargos, C. C. S. (2013): A multi-objective hierarchical calibration procedure for land surface-ecosystem models. Inverse Problems in Science and Engineering, 21(3), 357–386.

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Publicado

2016-07-20

Como Citar

Araújo, A. da S., Velho, H. de C., & Minjiao, L. (2016). MULTI-OBJECTIVE CALIBRATION OF IBIS MODEL BY GENETIC ALGORITHM WITH PARAMETRIC SENSITIVITY ANALYSIS. Ciência E Natura, 38, 90–97. https://doi.org/10.5902/2179460X20095

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