An alternative way to compute Fourier amplitude sensitivity test (FAST)
Computational Statistics & Data Analysis
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Parameter estimation and uncertainty analysis for a watershed model
Environmental Modelling & Software
Environmental Modelling & Software
Algebraic sensitivity analysis of environmental models
Environmental Modelling & Software
Identifiability analysis for receiving water body quality modelling
Environmental Modelling & Software
Assessment of data availability influence on integrated urban drainage modelling uncertainty
Environmental Modelling & Software
A bootstrap approach to assess parameter uncertainty in simple catchment models
Environmental Modelling & Software
ANN-DT: an algorithm for extraction of decision trees from artificial neural networks
IEEE Transactions on Neural Networks
Review: Three complementary methods for sensitivity analysis of a water quality model
Environmental Modelling & Software
Watershed model parameter estimation and uncertainty in data-limited environments
Environmental Modelling & Software
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In this paper, the issue of nonlinear sensitivity analysis for dimensionality reduction in hydrologic model calibration is discussed, and a novel method to quantify the sensitivity of each parameter that considers the nonlinear relationship in the model is presented. The method is based on computing the absolute variation of the nonlinear function represented by the model in its parameter space. The paper discusses the theoretical background of the method and presents the algorithm. The algorithm employs neural network as a pseudo simulator to reduce the computational burden of the analysis. The proposed approach of sensitivity analysis is illustrated through a case study on a physically based distributed hydrologic model. The results indicate that the method is able to rank the parameters effectively, and the ranking can be interpreted in the context of the physical processes being considered by the model.