In this study, the performance evaluation of five machine learning models, namely, ANNLM, ANNSCG, least square-support vector regression (LS-SVR), reduced error pruning tree (REPTree) and M5, was carried out for predicting runoff and sediment in the Pokhariya watershed, India using hydro-meteorological variables as input. The input variables were selected using the trial-and-error procedure which represents the hydrological process in the watershed. The seven input variables to all the models comprised a combination of rainfall, average temperature, relative humidity, pan evaporation, sunshine duration, solar radiation and wind speed. The monthly runoff and sediment yield data were used to calibrate and validate all models for the years 2000 to 2008. Evaluation of models' performances were carried out using four statistical indices, i.e., Nash–Sutcliffe coefficient (NSE), coefficient of determination (R2), percent bias (PBIAS) and RMSE-observations standard deviation ratio (RSR). Comparative analysis showed that the ANNLM model marginally outperformed the LS-SVR model and all the other models investigated during calibration and validation for runoff modelling whereas the LS-SVR model surpassed the artificial neural networks (ANN) model and other models for sediment yield modelling. Moreover, M5 model tree is better in simulating sediment yield and runoff than its near counterpart, the REPTree model, and marginally inferior when compared to LS-SVR and ANN models.

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