Generalized regression neural network in modelling river sediment yield

By H. Cigizoglu, Murat Alp. Advances in Engineering Software, 2006

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Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree (2010, background): “Successful applications of machine learning approaches in water resources management by Dawson and Wilby (1998), Govindaraju (2000), Solomatine and Dulal (2003), Bhattacharya and Solomatine (2005), Cigizoglu and Alp (2006) , Altunkaynak (2007), Hong and Pai (2007), Pal and Goel (2007), Singh and Ku”
Formulation of flow number of asphalt mixes using a hybrid computational method (2011, methodology): “The probability estimate will be the sum of those sample probabilities [50,51].”
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “modelling river sediment yield [33] or catchment water supply [79], predicting ozone concentration [147], modelling leachate flow rate [88] or estimating the nitrate distribution in groundwater [8].”
Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment (2016, methodology): “Hence, we apply here another neural network named the GRNN, which can overcome the shortcomings of the BPNN (Cigizoglu and Alp, 2006).”