Artificial neural network approach for modelling and prediction of algal blooms

By F. Recknagel, M. French, P. Harkonen, Ken-ichi Yabunaka. 1997

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Citations in the corpus

All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, methodology): “A similar “Perturb” VI measure has been used for neural networks, where noise is added to covariates (Recknagel et al., 1997; Yao et al., 1998; Scardi and Harding, 1999; Gevrey et al., 2003).”
Neural network and genetic programming for modelling coastal algal blooms (2006, methodology): “…neural network; Wei et al. (2001) included eight environmental factors to estimate the evolution of four dominant phytoplankton genera using ANN; Recknagel et al. (1997) used 10 input variables to a feed-forward ANN for the prediction of algal bloom in lakes in Japan, Finland and Australia;…”
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., 2”
Modelling allelopathy among marine algae (2005, background): “The study of fluctuations in phytoplankton abundance has been addressed by a variety of numerical models (Steele and Henderson, 1981; Fasham, 1995; Truscott, 1995; Baretta and Baretta-Bekker, 1997; Recknagel et al., 1997).”
Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters (2003, background): “…Levine et al., 1996), patterning complex relationships (Lek et al., 1996; Tuma et al., 1996), predicting population and community development (Tan and Smeins, 1996; Recknagel et al., 1997; Chon et al., 2000), and modelling habitat suitability (Paruelo and Tomasel, 1997; Özesmi and Özesmi, 1999).”
Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium) (2004, methodology): “The prediction of aquatic communities by means of ANN models has recently been discussed by several authors (Brosse et al., 1999; Guegan et al., 1998; Hoang et al., 2001; Karul et al., 2000; Lae et al., 1999; Lee et al., 2003; Lek et al., 1996; Maier and Dandy, 1997; Maier and Dandy, 2001; Maier et ”
Developing an empirical model of phytoplankton primary production: a neural network case study (1999, background): “Since that initial report, further research has been carried out on primary production and ancillary data for Chesapeake Bay spanning over a decade (Harding et al., in prep.) and on the application of neural networks both to phytoplankton production modelling (Scardi, in prep.) and to other related ”
All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, background): “The Rashomon effect has also been considered in several subject areas outside of VI, including those in non-statistical academic disciplines (Heider, 1988; Roth and Mehta, 2002).”