Fuzzy regression is considered one of the most important regression models, and recently the fuzzy regression model has become a powerful tool for conducting statistical operations, however, the above model also faces some problems and violations, including (when the data is skewed, or no-normal, .....) and thus leads to incorrect results, so it is necessary to find a model to deal with such violations and problems suffered by the regular fuzzy regression models and at the same time be more powerful and immune than the fuzzy regression model called the semi-parametric fuzzy quantile regression. This model is characterized by containing two parts, the first is the fuzzy parametric part (fuzzy inputs and crisp parameters) and the second is the fuzzy nonparametric part for fuzzy triangular numbers, and the semiparametric fuzzy quantile regression is estimated. To demonstrate the effectiveness of our combining model, we will utilize the following Akbari and Hesamian (2019) dataset that was used as a reference case study. Estimate Fuzzy Quantile Regression Model: (FQRM), Fuzzy semi-parametric quantile regression: (FSPQRM), Fuzzy Support Vector Machine: (FSVM), Combining FQRM-FSVR (Comb), Combining FSPQRM-FSVR. Using a new metric measure Jensen–Shannon Distance: (JS) based on fuzzy belonging functions. Two criteria MSM and G1 were used in comparison.
