| Summary: | Accurate monthly runoff prediction is still challenging work regardless of the accessibility of different modelling techniques, like the knowledge-driven or data-driven models, and human activities and climate changes. To this context, applicability of hybrid SVM-SSA (Support Vector Machine with Salp Swarm Algorithm) model and conventional SVM and artificial neural network (ANN) models is investigated for runoff prediction in Baitarani river basin, Odisha, India. Potential of proposed techniques is measured utilising four quantitative indexes, root mean squared error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and Willmott index (WI). Test results specify that hybrid model generates better prediction accurateness in comparison to applied conventional methods. The generalization and robustness of SVM-SSA techniques were very prominent, with R2 values of 0.9847 and 0.9844 for Anandpur and Champua stations during training phases. Similarly prominent value of WI are 0.9906, 0.9902 and minimum value of RSE and MAE are 20.019, 0.5928 and 0.0769, 0.5934 for Anandpur and Champua stations respectively. Therefore, SVM-SSA can be recommended for modeling complexity of interactions for rainfall-runoff process and predicting runoff.
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