A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

Environmental Modelling and Software
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Abstract

This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty
Series title Environmental Modelling and Software
DOI 10.1016/j.envsoft.2011.07.014
Volume 26
Issue 12
Year Published 2011
Language English
Publisher Elsevier Science
Description 16 p.
First page 1583
Last page 1598