A hydraulic jump phenomenon serves a variety of purposes, for instance, to dissipate the energy of flow to prevent bed erosion and aerate water or to facilitate the mixing process of chemicals used for the purification of water. In the current study, three artificial intelligence approaches, namely artificial neural networks (ANNs), two different adaptive-neuro-fuzzy inference system with grid partition (ANFIS-GP), and gene expression programming (GEP) were applied to forecast developed and non-developed hydraulic jump length. Four different GEP, ANFIS-GP and ANN models comprising various combinations of Froude number, bed roughness height, upstream and downstream flow depth based on measured experimental data-set were developed to forecast hydraulic jump length variations. The determination coefficient (R2) and root mean square error (RMSE) statistics were used for evaluating the accuracy of models. Based on the comparisons, it was found that the ANN, ANFIS-GP and GEP models could be employed successfully in forecasting hydraulic jump length. A comparison was made between these artificial intelligence approaches which emphasized the superiority of ANNs and ANFIS-GP over the other intelligent models for modeling developed and non-developed hydraulic jump length, respectively. For non-developed hydraulic jump, the R2 and RMSE values obtained as 0.87 and 2.84 for ANFIS-GP model.
