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

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@Article{Friedel20111583,

• author = "Michael J. Friedel",
• title = "A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty",
• journal = "Environmental Modelling \& Software",
• volume = "26",
• number = "12",
• pages = "1583--1598",
• year = "2011",
• ISSN = "1364-8152",
• DOI = "doi:10.1016/j.envsoft.2011.07.014",
• URL = "http://www.sciencedirect.com/science/article/pii/S1364815211001757",
• keywords = "genetic algorithms, genetic programming, Wildfire, Debris-flow volume, Self-organising map, Multivariate, Prediction, Nonlinear models, Nonlinear uncertainty",
• 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 minimises root-mean squared and unit errors for the evolution of fittest equations. An optimisation 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 characterised 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 modelling approach can be applied to nonlinear multivariate problems in all fields of study.",

}

Genetic Programming entries for Michael J Friedel

Citations