A generalised approach for identifying influential data in hydrological modelling - IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (<b>anciennement Cemagref</b>)
Article Dans Une Revue Environmental Modelling and Software Année : 2019

A generalised approach for identifying influential data in hydrological modelling

D. Wright
  • Fonction : Auteur correspondant
  • PersonId : 1089079

Connectez-vous pour contacter l'auteur
M. Thyer
S. Westra
D. Mcinerney

Résumé

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 (
Fichier principal
Vignette du fichier
hdl_124123.pdf (2.99 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02608443 , version 1 (12-09-2024)

Identifiants

Citer

D. Wright, M. Thyer, S. Westra, Benjamin Renard, D. Mcinerney. A generalised approach for identifying influential data in hydrological modelling. Environmental Modelling and Software, 2019, 111, pp.231-247. ⟨10.1016/j.envsoft.2018.03.004⟩. ⟨hal-02608443⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

More