Ian Guymer

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Evaluation of longitudinal mixing processes in open channel flows is important in environmental management, requiring the quantification of mixing coefficients. Estimates of these coefficients sufficiently accurate for environmental impact assessments cannot be achieved using current theoretical or semi-empirical methods for natural channels. This(More)
River ecosystems are influenced by contaminants in the water column, in the pore water and adsorbed to sediment particles. When exchange across the sediment-water interface (hyporheic exchange) is included in modeling, the mixing coefficient is often assumed to be constant with depth below the interface. Novel fiber-optic fluorometers have been developed(More)
AbSTRACT In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs) to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), which simplifies the neural(More)
Longitudinal dispersion coefficient is a key variable for the description of the longitudinal transport in a river. In recent years, Artificial Neural Networks (ANNs) have become popular and useful tools for environmental modellers as they are perceived to overcome some of the difficulties associated with traditional statistical approaches. In these(More)
Results from previous solute tracer laboratory experiments across circular surcharged manhole structures by Guymer et al. have been used to optimise parameters within Hart's transient storage model (TSM). A surcharge threshold level for the model parameters is evident and this is explained in relation to jet theory. The ability to decompose the TSM is(More)
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