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|Title:||A generalised approach for identifying influential data in hydrological modelling|
|Citation:||Environmental Modelling and Software, 2019; 111:231-247|
|David P. Wright, Mark Thyer, Seth Westra, Benjamin Renard, David McInerney|
|Abstract:||Influence diagnostics are used to identify data points that have a disproportionate impact on model parameters, performance and/or predictions, providing valuable information for use in model calibration. Regression-theory influence diagnostics identify influential data by combining the leverage and the standardised residuals, and are computationally more efficient than case-deletion approaches. This study evaluates the performance of a range of regression-theory influence diagnostics on ten case studies with a variety of model structures and inference scenarios including: nonlinear model response, heteroscedastic residual errors, data uncertainty and Bayesian priors. A new technique is developed, generalised Cook's distance, that is able to accurately identify the same influential data as standard case deletion approaches (Spearman rank correlation: 0.93–1.00) at a fraction of the computational cost (<1%). This is because generalised Cook's distance uses a generalised leverage formulation which outperforms linear and nonlinear leverage formulations because it has less restrictive assumptions. Generalised Cook's distance has the potential to enable influential data to be efficiently identified on a wide variety of hydrological and environmental modelling problems.|
|Keywords:||Hydrologic model calibration; Influence diagnostics; Cook's distance; Generalised leverage|
|Description:||Available online 22 June 2018|
|Rights:||© 2018 Elsevier Ltd. All rights reserved.|
|Appears in Collections:||Civil and Environmental Engineering publications|
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