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Water Level Forecasting Through Fuzzy Logic and Artificial Neural Network Approaches : Volume 10, Issue 1 (08/02/2006)

By Alvisi, S.

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Book Id: WPLBN0004010060
Format Type: PDF Article :
File Size: Pages 17
Reproduction Date: 2015

Title: Water Level Forecasting Through Fuzzy Logic and Artificial Neural Network Approaches : Volume 10, Issue 1 (08/02/2006)  
Author: Alvisi, S.
Volume: Vol. 10, Issue 1
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Mascellani, G., Franchini, M., Bárdossy, A., & Alvisi, S. (2006). Water Level Forecasting Through Fuzzy Logic and Artificial Neural Network Approaches : Volume 10, Issue 1 (08/02/2006). Retrieved from

Description: Dipartimento di Ingegneria, Università degli Studi di Ferrara, Italia. 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.

Water level forecasting through fuzzy logic and artificial neural network approaches


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