Application of an evolutionary algorithm for parameter optimization in a gully erosion model. (June 2016)

• Record Type: Journal Article
• Authors: Rengers, Francis; Lunacek, Monte; Tucker, Gregory
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 297
• Page End: 305
• Publication Date: 2016-06
• Subjects: Optimization -- Erosion -- Hydrology -- Landscape modeling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.033
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Proof-of-concept demonstrating the application of an evolutionary optimization technique to a landform model. Inverse approach applied to elucidate model boundary conditions. Prediction of watershed-scale hydrologic and sedimentologic parameters.