The coherence between different aspects in the environmental system leads to a demand for comprehensive models of this system to explore the effects of different management alternatives. Fuzzy logic has been suggested as a means to extend the application domain of environmental modelling from physical relations to expert knowledge. In such applications the expert describes the system in terms of fuzzy variables and inference rules. The result of the fuzzy reasoning process is a numerical output value. In such a model, as in any other, the model context, structure, technical aspects, parameters and inputs may contribute uncertainties to the model output. Analysis of these contributions in a simplified model for agriculture suitability shows how important information about the accuracy of the expert knowledge in relation to the other uncertainties can be provided. A method for the extensive assessment of uncertainties in compositional fuzzy rule-based models is proposed, combining the evaluation of model structure, input and parameter uncertainties. In an example model, each of these three appear to have the potential to dominate aggregated uncertainty, supporting the relevance of an ample uncertainty approach.
In densely populated delta areas, water management requires balancing of many different interests and user functions. Because of the many different actors, and interaction with the physical environment, governed by many different physical processes, and the need for knowledge from many different areas, the decision making process becomes very complex. To support the decision and policy making process, different tools are utilized. Among these are software tools, where collected data and analytical models serve, for instance, to explore different policy options, analyse real time events, or predict future states of the system configuration. The fact that not all desired information can be described in physical terms may restrict the application of such models. Sometimes experts may be able to provide valuable additional information. In such cases the application of fuzzy rule-based models can be an option (Adriaenssens et al., 2004; Ascough et al., 2008). As in any other environmental modelling approach, it is important to address the uncertainty in the model’s output. This uncertainty assessment is the result of the conceptualization of expert knowledge in a fuzzy rule-based model.
Publisher : ELSEVIER
By : J.A.E.B. Janssen , M.S. Krol , R.M.J. Schielen , A.Y. Hoekstra a, J.-L. de Kok
File Information: English Language/ 7 Page / size: 452 KB
سال : 2010
ناشر : ELSEVIER
کاری از : J.A.E.B. Janssen , M.S. Krol , R.M.J. Schielen , A.Y. Hoekstra , J.-L. de Kok
اطلاعات فایل : زبان انگلیسی / 7صفحه / حجم : KB 452
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