Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations

94 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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): “…in model inputs and outputs, model structure and model parameters, which has been addressed in a number of studies (Kingston et al., 2005a; Khan and Coulibaly, 2006; Srivastav et al., 2007; Jana et al., 2008; Kingston et al., 2008; Zhang et al., 2009, 2011; Kasiviswanathan et al., 2013).”
Hydrological uncertainty processor based on a copula function (2018, methodology): “The trade-off between reliability and sharpness has been discussed in previous research (Xiong et al. 2009, Li et al. 2010a, Kasiviswanathan et al. 2013), which showed that these two desirable objectives could not be achieved simultaneously.”