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Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: Application to the calibration of the Sacramento soil‐moisture accounting model
Author(s) -
Chu Wei,
Gao Xiaogang,
Sorooshian Soroosh
Publication year - 2010
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2010wr009224
Subject(s) - subspace topology , population , nonlinear system , watershed , calibration , principal component analysis , degenerate energy levels , parameter space , mathematical optimization , mathematics , computer science , soil science , algorithm , statistics , environmental science , artificial intelligence , machine learning , physics , demography , quantum mechanics , sociology
An innovative algorithm, shuffled complexes with principal components analysis (SP‐UCI), is developed to overcome a critical deficiency of the shuffled complex evolution scheme: population degeneration. Population degeneration means that, during the evolutionary search process, the population of search particles may degenerate into a subspace of the full parameter space, thereby missing the capacity of fully exploring the parameter space. Being confined in a subspace may even lead the particle population to converge to nonstationary points, which is a fatal malfunction. To overcome this problem, SP‐UCI employs the principal components analysis to detect the occurrence of population degeneration and remedy the adverse effects. The ensemble of calibrations of the Sacramento soil moisture accounting model with the SP‐UCI method over the Leaf River basin, Mississippi, retrieves the optimal parameter values with the lowest recorded root‐mean‐squared error of simulated daily runoff against the observation. Moreover, the result also provides consistent (narrow ranges) model parameter distribution, which results in a better understanding of the model's behavior, given the watershed's hydrologic features.