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A Computational Information Criterion for Particle-Tracking with Sparse or Noisy Data by Tran, Nhat Thanh V. (author);Benson, David A. (author);Schmidt, Michael J. (author);Pankavich, Stephen D. (author) is a Environmental Science article available to read on EtoBox.

What is A Computational Information Criterion for Particle-Tracking with Sparse or Noisy Data about?

Traditional probabilistic methods for the simulation of advection-diffusion equations (ADEs) often overlook the entropic contribution of the discretization, e.g., the number of particles, within associated numerical methods. Many times, the gain in accuracy of a highly discretized numerical model is outweighed by its associated computational costs or the noise within the data. We address the question of how many particles are needed in a simulation to best approximate and estimate parameters in one-dimensional advective-diffusive transport. To do so, we use the well-known Akaike Information Criterion (AIC) and a recently-developed correction called the Computational Information Criterion (COMIC) to guide the model selection process. Random-walk and masstransfer particle tracking methods are employed to solve the model equations at various levels of discretization. Numerical results demonstrate that the COMIC provides an optimal number of particles that can describe a more efficient model in terms of parameter estimation and model prediction compared to the model selected by the AIC even when the data is sparse or noisy, the sampling volume is not uniform throughout the physical dom

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Author

Tran, Nhat Thanh V. (author);Benson, David A. (author);Schmidt, Michael J. (author);Pankavich, Stephen D. (author)

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)