Characterising performance of environmental models

By N. Bennett, Barry F. W. Croke, G. Guariso, Joseph H. A. Guillaume, S. Hamilton, Anthony J. Jakeman, Stefano Marsili-Libelli, Lachlan T. H. Newham, John P. Norton, Charles Perrin and 6 more. 2013

1,458 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Ocean Engineering, Water Science and Technology

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “In hydrological modeling, the choice of objective function (i.e., performance metrics) can have a great impact on the identifiability of model parameters (Bennett et al., 2013).”
A Global Lake Ecological Observatory Network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models (2015, positive): “The wealth of data arising from this network offers new opportunities to improve ecological modelling methods (Beck et al. 2009, Trolle et al. 2011a, Bennett et al. 2012).”
Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, result): “For this reason, recent studies (e.g. Bennett et al., 2013; Lapuschkin et al., 2019) advocated the use of other criteria to measure the overall performance, such as model complexity or consistency (Karpatne et al., 2017).”
Evaluating process-based integrated assessment models of climate change mitigation (2017, background): “These indicators classify model behavior under harmonized forcings or scenario assumptions (99).”
Crop modelling for integrated assessment of risk to food production from climate change (2015, methodology): “Finally, calibration and evaluation of the entire IAM is generally impossible due to large data requirements, and alternate measures should be devised to evaluate them appropriately (Parker et al., 2002), such as sub-model testing and plausibility analysis, among others (Bennett et al., 2013).”
An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, positive): “As with model performance evaluation (Bennett et al., 2013), the most fitting choice is always case-dependent, depending on the available models as well as the decision problem at hand.”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, methodology): “While this has been acknowledged in a wider modelling context [e.g. Gass et al., 1981; Refsgaard et al., 2005; Jakeman et al., 2006; Bennett et al., 2013], it has received little attention in the literature on the development of ANNs for environmental and water resources modelling.”
A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, neutral): “They are the correlation coefficient (R), root-mean-square error (RMSE), and Nash–Sutcliffe model efficiency coefficient (NSE) [Nash and Shutcliff, 1970; Dawson et al., 2007; Bennett et al., 2013].”
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, methodology): “3 Accuracy (ACC) ACC ¼ TPþTNTPþTNþFPþFN Accuracy is the proportion of landslide and non-landslide pixels which are correctly classified (Bennett et al., 2013).”
Application of the HEC-HMS model for runoff simulation in a tropical catchment (2013, methodology): “In addition the validated model was evaluated by residual method (Bennett et al., 2013; Pauly, 1980).”