Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Parameter estimation and uncertainty analysis for a watershed model
Environmental Modelling & Software
Environmental Modelling & Software
Journal of Artificial Evolution and Applications - Regular issue
Environmental Modelling & Software
Environmental Modelling & Software
Flood forecasting in transboundary catchments using the Open Modeling Interface
Environmental Modelling & Software
Estimation of water and salt generation from unregulated upland catchments
Environmental Modelling & Software
IEEE Transactions on Pattern Analysis and Machine Intelligence
Environmental Modelling & Software
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Environmental Modelling & Software
Forecasting conditional climate-change using a hybrid approach
Environmental Modelling & Software
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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.