## 1 Introduction
The Latin hypercube sampling (LHS) method, as a uniform sampling method, was used to ensure that sampling areas can be uniformly covered by all sampling points [24]. In this work, the sampling points represent the combinations of the three parameters (\(C\), \(\gamma\) and \(\zeta\)) in three-dimensional space. There are two advantages of LHS. Firstly, the sampling points generated by LHS can effectively fill the sampling space. Secondly, less sampling points generated by LHS can represent more combinations of parameters. In this work, a novel SVR for the flow behaviors of as-forged Ti-10V-2 Fe-3 Al alloy combined with LHS was established, i.e., the LHS-SVR. Compared to the work of [PERSON] et al. [23] the sampling points generated by LHS-SVR can effectively fill the sampling space and represent more combinations of the three parameters (\(C\), \(\gamma\) and \(\zeta\)). LHS-SVR can show the influences of the three parameter combinations on the accuracy of the model, and this influence law can be used as a reference to researchers when they need to select the three parameters. However, LHS-SVR needs to calculate the many parameter combinations in the search space, and there is still room for improvement in efficiency. GA, as a global optimization algorithm, has been widely used in the multi-parameter optimization area on account of the advantages of parallel processing, high efficiency and strong robustness. GA searches the optimal parameters in the solution space by imitating the natural selection process and genetic mechanism. In order to use the advantages of GA, an SVR model of the hot flow behaviors of as-forged Ti-10V-2 Fe-3 Al alloy combined with GA was constructed, i.e., the GA-SVR. In GA-SVR, GA was utilized to efficiently seek the optimal parameter combination of the three parameters (\(C\), \(\gamma\) and \(\zeta\)). The GA-SVR only requires representative training samples from the research problem and then self-adaptively and dynamically adjusts the three parameters (\(C\), \(\gamma\) and \(\zeta\)) to attain the most accurate SVR. In this work, the comparisons of the learning abilities, generalization abilities and modelling efficiencies of the mathematical regression model, ANN, LHS-SVR and GA-SVR were investigated. A standard statistical parameter, average absolute relative error (\(AARE\)), was utilized to estimate the predicted performance of these four prediction models. In the comparisons of study abilities, the LHS-SVR and GA-SVR have larger \(R\)-values and lower \(AARE\)-values, which show that the LHS-SVR and GA-SVR can sufficiently and accurately learn the training samples. The study abilities of these models were shown as follows in ascending order: ANN < GA-SVR < LHS-SVR. In the comparisons of the generalization abilities, the LHS-SVR and GA-SVR have larger \(R\)-values and lower \(AARE\)-values, which indicate that the LHS-SVR and GA-SVR can accurately predict the highly non-linear flow behaviors. The generalization abilities of these four models were shown as follows in ascending order: the mathematical regression model < ANN < GA-SVR < LHS-SVR. ANN can meet well the network topology and training parameters to achieve a higher accuracy level; nevertheless, these accurate results have poor reproducibility. The significant character of SVR is that an SVR with identical training parameters will maintain training accuracy and prediction accuracy at stable levels in different attempts for a certain dataset. Besides, the \(R\)-values between the training samples and fitted values of LHS-SVR and GA-SVR of the Ti-10V-2 Fe-3 Al alloy are larger than 0.9999, showing that the fitting precisions of LHS-SVR and GA-SVR are larger than the empirical/semiempirical model and the analytical model. The computing time of a training process of LHS-SVR is greatly shorter than ANN.