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Advances in Fuzzy Systems
Volume 2013, Article ID 131875, 10 pages
http://dx.doi.org/10.1155/2013/131875
Research Article

Mining Linguistic Associations for Emergent Flood Prediction Adjustment

Institute for Research and Applications of Fuzzy Modeling, National Supercomputing Center IT4Innovations, Division of University of Ostrava, 30. dubna 22, 701 03 Ostrava, Czech Republic

Received 13 October 2013; Accepted 19 October 2013

Academic Editor: Salvatore Sessa

Copyright © 2013 Michal Burda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Floods belong to the most hazardous natural disasters and their disaster management heavily relies on precise forecasts. These forecasts are provided by physical models based on differential equations. However, these models do depend on unreliable inputs such as measurements or parameter estimations which causes undesirable inaccuracies. Thus, an appropriate data-mining analysis of the physical model and its precision based on features that determine distinct situations seems to be helpful in adjusting the physical model. An application of fuzzy GUHA method in flood peak prediction is presented. Measured water flow rate data from a system for flood predictions were used in order to mine fuzzy association rules expressed in natural language. The provided data was firstly extended by a generation of artificial variables (features). The resulting variables were later on translated into fuzzy GUHA tables with help of Evaluative Linguistic Expressions in order to mine associations. The found associations were interpreted as fuzzy IF-THEN rules and used jointly with the Perception-based Logical Deduction inference method to predict expected time shift of flow rate peaks forecasted by the given physical model. Results obtained from this adjusted model were statistically evaluated and the improvement in the forecasting accuracy was confirmed.