In the current paper, the efficiency of three new standalone data mining algorithms [e.g., M5P, Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of Bagging, BA (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier, ASC (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from 1979-2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all the data were used (i.e., R and Q –with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, the BA-M5P outperformed the other models.

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