Optimal experimental design for practical identification of unstructured growth models
Selected papers from the 2nd IMACS symposium on Mathematical modelling---2nd MATHMOD
Environmental Modelling & Software
PEAS: A toolbox to assess the accuracy of estimated parameters in environmental models
Environmental Modelling & Software
Identifiability analysis for receiving water body quality modelling
Environmental Modelling & Software
Application of chaos and fractal models to water quality time series prediction
Environmental Modelling & Software
Environmental Modelling & Software
Preface: Modelling and automation of water and wastewater treatment processes
Environmental Modelling & Software
Modelling microbial population dynamics in nitritation processes
Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
Environmental Modelling & Software
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Water quality modelling in small rivers is often considered unworthy from a practical and economic viewpoint. This paper shows instead that a simple model structure can be set up to describe the stationary water quality in small river basins in terms of carbon and nitrogen compounds, when the use of complex models is unfeasible. In short rivers point and nonpoint sources play a key role in shaping the model response, being as important as the self-purification dynamics. Further, the varying river characteristics, in terms of morphology, hydraulics and vegetation, require the introduction of variable parameters, thus complicating the originally simple model structure. To determine the identifiability of the resulting model an identifiability assessment was carried out, based on sensitivity analysis and optimal experiment design criteria. The identifiable subset was determined by ranking the parameters in terms of sensitivity and computing the associated Fisher Information Matrices. It was found that the inclusion of the nonpoint sources as piecewise constant parameters affected the identifiability to a considerable extent. However, the combined parameter-sources calibration was made possible by the use of a robust estimation algorithm, which also provided estimation confidence limits. The calibrated model responses are in good agreement with the data and can be used as scenario generators in a general strategy to conserve or improve the water quality.