In this article, an adjustment of the extreme theoretical probability distributions upon the sample is proposed, based on the conventional fuzzy linear regression model of Tanaka [1], where all the data must be included within the produced fuzzy band. This is achieved by using the quintile approach, which relates the observed return period with the theoretical cumulative probability. A new contribution of this work is the use of the fuzzified maximum likelihood, as a measure of goodness of fit. The model is applied for real data from the Strymonas River, regarding the annual maximum flow, and finally, useful conclusions are made.

The objective of this study is to transform the arithmetic coefficients of the total sediment transport rate formula of Yang into fuzzy numbers, and thus create a fuzzy relationship that will provide a fuzzy band of in-stream sediment concentration. A very large set of experimental data, in flumes, was used for the fuzzy regression analysis. In a first stage, the arithmetic coefficients of the original equation were recalculated, by means of multiple regression, in an effort to verify the quality of data, by testing the closeness between the original and the calculated coefficients. Subsequently, the fuzzy relationship was built up, utilizing the fuzzy linear regression model of Tanaka. According to Tanaka’s fuzzy regression model, all the data must be included within the produced fuzzy band and the non-linear regression can be concluded to a linear regression problem when auxiliary variables are used. The results were deemed satisfactory for both the classic and fuzzy regression-derived equations. In addition, the linear dependence between the logarithmized total sediment concentration and the logarithmized subtraction of the critical unit stream power from the exerted unit stream power is presented. Ultimately, a fuzzy counterpart of Yang’s stream sediment transport formula is constructed and made available to the readership.

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