Mathematical models are extensively used to support decision-making in many disciplines. Nevertheless there are not clear standard guidelines to assess models performance. This significantly affects model selection processes, which aim to determine the "best model", among several possible candidates. Model performance is often measured by the accuracy with which models predictions fit independent observations. However this test assesses only a single aspect of a model. A model selection process should establish the similarities between the constructed and the conceptual model. Therefore it should be based on a comprehensive assessment of the models capabilities, which is the objective of the multi-aspect comparison approach proposed in this work. The innovative aspect of this approach is to create a relationship among four conventional tests, i.e. uncertainty and sensitivity analysis, goodness-of-fit prediction-observations, model complexity and level of details, in order to provide a reliable estimation of the differences between the constructed and conceptual models. Although, model complexity is quantified using a standard approach, a novel methodology is proposed in this thesis, intended to be an intuitive and illustrative approach in creating a linkage between model complexity and level of detail. Five radioecological models have been considered: SAVE rural model, TEMAS rural model, SAVE semi-natural model, FORM and RIFE1. The results show that there is a limited resemblance between these models and the respective conceptual models. This is due to low prediction accuracy (RIFE1 and FORM); high level of uncertainty (SAVE rural); sensitivity to parameters which is not consistent with the current understanding of radiocaesium behaviour in the environment (TEMAS and SAVE rural). The SAVE rural model has been revisited in order to increase the similarity between the constructed and conceptual model. The resulting model prediction shows lower degree of uncertainty and there is a significant agreement between the model sensitivity results and the general understanding of the processes affecting Cs soil-to-plant transfer. Nonetheless the revised model does not show higher prediction accuracy than the original model. It is concluded that a reliable methodology for model selection should be based on a comprehensive investigation of each considered model aspect and that there is not a single best approach. The methodology proposed in this work has been successful in the case of the five radioecological models studied.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:514636 |
| Date | January 2005 |
| Creators | Tarsitano, Davide |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/10202/ |
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