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Title: Application of an Evolutionary Algorithm for Parameter Optimization in a Gully Erosion Model

Herein we demonstrate how to use model optimization to determine a set of best-fit parameters for a landform model simulating gully incision and headcut retreat. To achieve this result we employed the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an iterative process in which samples are created based on a distribution of parameter values that evolve over time to better fit an objective function. CMA-ES efficiently finds optimal parameters, even with high-dimensional objective functions that are non-convex, multimodal, and non-separable. We ran model instances in parallel on a high-performance cluster, and from hundreds of model runs we obtained the best parameter choices. This method is far superior to brute-force search algorithms, and has great potential for many applications in earth science modeling. We found that parameters representing boundary conditions tended to converge toward an optimal single value, whereas parameters controlling geomorphic processes are defined by a range of optimal values.
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Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1364-8152
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Environmental Modelling and Software; Journal Volume: 80; Related Information: Environmental Modelling and Software
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
National Science Foundation (NSF)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; optimization; erosion; hydrology; landscape modeling