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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 1 | Copyright
Hydrol. Earth Syst. Sci., 10, 1-17, 2006
https://doi.org/10.5194/hess-10-1-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  08 Feb 2006

08 Feb 2006

Water level forecasting through fuzzy logic and artificial neural network approaches

S. Alvisi1, G. Mascellani1, M. Franchini1, and A. Bárdossy2 S. Alvisi et al.
  • 1Dipartimento di Ingegneria, Università degli Studi di Ferrara, Italia
  • 2Institut für Wasserbau, Universität Stuttgart, Germany

Abstract. In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively.

All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time.

The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy). It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.

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