In this present study, a novel approach is introduced for accurate prediction of the incipient motion of uniform grain particles in sand and gravel-bedded open channels under unidirectional flow by improving Sugeno Fuzzy Inference System (Sugeno FIS). The Adaptive Neural Fuzzy Inference System (ANFIS) tool is based on Sugeno FIS. Consequent part of the ANFIS tool is limited to either as a constant or a linear function. This means that not only a non-linear function is available for the consequent part of the model but also it cannot be represented by the constant and linear functions, at the same time. ANFIS tool optimizes antecedent parameters (fuzzy sets) and consequent parameters (constant or linear functions) by utilizing neural and least square methods, respectively. In this study, a novel hybrid model named as Geno-Fuzzy Inference System (GENOFIS) is introduced by integrating improved Sugeno FIS and Genetic Algorithms (GAs) that refer to where, the antecedent (fuzzy sets) and consequent (constant, linear and non-linear functions) parameters are optimized using Genetic Algorithms (GAs) tool. A quantitative comparison is implemented between the ANFIS and GENOFIS models using root mean square error (RMSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as evaluation criteria by using three different types of incipient motion of sediment measurements that exist in the literature as reference, visual and development of competence functions. The results of this present study demonstrated that the novel GENOFIS model provided more accurate prediction results in comparisons with the ANFIS model results for three different types of incipient motion of sediment.