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 highperformance 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. Published by Elsevier Ltd. Software availability Name of software: GullyErosionProfiler1D Developer: Francis Rengers Contact address: U.S. Geological Survey, 1711 Illinois St., Golden, CO 80401. [email protected] Year first available: 2012 Required software: Matlab Source: https://github.com/csdms-contrib/gullyerosionprofiler1d Availability and cost: GNU licensed freeware Name of software: Paramscan Developer: Monte Lunacek Contact address: National Renewable Energy Laboratory, 15013 DenverW, Pkwy, Golden, CO 80401. Monte.Lunacek@nrel. gov Year first available: 2014 Required software: Python Program Language: Python Source: https://github.com/frengers/JanusOpt/tree/master/src Availability and cost: GNU licensed freeware