A dynamic two-level artificial neural network (DTLANN) approach is used for the estimation of parameters in combined mode conduction-radiation heat transfer in a porous medium. Four commonly used neural networks: feed forward, cascade forward, fitnet, and radial basis are used in mapping artificial neural network (ANN), and their performance is compared under noisy big data (10,302 × 1300 matrix size).Governing equations for heat transfer in the porous medium through conduction and radiation modes are solved by finite volume method and discrete transfer method. This numerical model is called a direct model. A large amount of data is generated by using the direct model for different values of extinction coefficient β and convective coupling P 2 . These data were divided into different groups (class) based on the temperature difference between the gas and solid phase. In the inverse analysis, a

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