Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications

By Holger R. Maier, Graeme C. Dandy. Environmental Modelling & Software, 2000

2,376 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/4aa5d73c67c915b62cb3843b67031c249dc5e82f

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Citations in the corpus

Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, background): “It is usually preferred that the number of ANN parameters be less (or much less) than p as discussed in Maier and Dandy (2000), although mathematically there is no limitation when the number of parameters is higher than p.”
Review and comparison of methods to study the contribution of variables in artificial neural network models (2003, methodology): “ANN models are able to make perfect predictions and are recognised as powerful in this field (Skelton et al., 1995; Recknagel et al., 1997; Whitehead et al., 1997; Lu et al., 1998; Mastrorillo et al., 1998; Yang et al., 1998; Brosse et al., 1999; Lae et al., 1999; Lek et al., 1999; Liong et al., 200”
Modelling the dynamics of the evapotranspiration process using genetic programming (2007, methodology): “More information on the application of ANNs in water related studies can be found in the ASCE Task Committee on the Application of Artificial Neural Networks in Hydrology (2000) and in Maier & Dandy (2000).”
Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods (2014, background): “…2001; Daliakopoulos et al. 2005; Govindaraju and Rao 2000; Hartmann et al. 2007; Jain and Kumar 2007; Krishna et al. 2008; Lliadis and Maris 2007; Maier and Dandy 2000; May and Sivakumar 2009; Mutlu et al. 2008; Nayak et al. 2006; Panagoulis 2006; Ramirez et al. 2005; Riad et al. 2004;…”
Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium) (2004, background): “ Maier and Dandy (2000) mentioned that network architecture is generally highly problem dependent.”
ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING (2001, methodology): “Details of the ANN modelling process and development are beyond the (e.g. Kartam 1994; Maier and Dandy 2000) .”
HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts (2007, background): “For reviews of neural network applications in the hydrological sciences see: ASCE (2000), Dawson and Wilby (2001), Maier and Dandy (2000).”
Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes (2013, background): “According to Maier and Dandy (2000) if extrapolating beyond the range of the training data is needed it is recommended to use sigmoiadal-type transfer functions in the hidden layers and linear transfer functions in the output layer.”