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This paper presents a fuzzy interval linear regression method to combine climate temperature models. The study is carried out using air temperature data recorded yearly during the 20th century in the La Plata Basin. The objective of the study is to provide realistic predictions of the air temperature in the 21st century, taking into account five climate models to envelope the predicted data. The input to the fuzzy interval model is the central value for each climate model. The output observed data if the central value, the lower and upper limits, representing 90% of the dataset within a region. The output to the fuzzy interval regression model represents the uncertainty in a trapezoid shaped membership function, in which the core interval envelop all the observed central data values and the support interval envelopes 90% of observed data. The fuzzy regression parameters may be trapezoid shaped or crisp values and are computed such that the global uncertainty is minimized. A standard linear regression model is also be used for comparison and validation. The method has shown to be useful to handle the uncertainty management in climate model better than the linear regression despite its wider uncertainty range in all cases.
Date of Conference: 18-23 July 2010