TY - JOUR
T1 - Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models
AU - Janssen, J.A.E.B.
AU - Krol, M.S.
AU - Schielen, R.M.J.
AU - Hoekstra, A.Y.
AU - de Kok, J.-L.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Expert knowledge
KW - Uncertainty Analysis
KW - Fuzzy Logic
U2 - 10.1016/j.ecolmodel.2010.01.011
DO - 10.1016/j.ecolmodel.2010.01.011
M3 - Article
SN - 0304-3800
VL - 221
SP - 1245
EP - 1251
JO - Ecological modelling
JF - Ecological modelling
IS - 9
ER -