Successful daily river flow forecasting is necessary in water resources planning and management. A reliable rainfall-runoff model can provide useful information for water resources planning and management. In this study, particle swarm optimization algorithm (PSO) as a metaheuristic approach is employed to train artificial neural network (ANN). The proposed PSO-ANN model is applied to simulate the rainfall runoff process in Karaj River for one and two days ahead. In this regard, different combinations of the input variables including flow and rainfall time series in previous days have been taken under consideration in order to obtain the best model's performances. To evaluate efficiency of the PSO algorithm in training ANNs, separate ANN models are developed using Levenberg-Marquardt (LM) training algorithm and the results are compared with those of the PSO-ANN models. The comparison reveals superiority of the PSO algorithm than the LM algorithm in training the ANN models. The best model for 1 and 2 days ahead runoff forecasting has R2 of 0.88 and 0.78. Results of this study shows that a reliable prediction of runoff in 1 and 2 days ahead can be achieved using PSO-ANN model. Overall, results of this study revealed that an acceptable prediction of the runoff up to two days ahead can be achieved by applying the PSO-ANN model.

River flow forecasting is important for successful water resources planning and management. The current study investigated the applicability of the artificial neural network (Ann), adaptive neuro-fuzzy inference system (Anfis), wavelet-Ann (Wann) and wavelet-Anfis (Wanfis) for daily river flow forecasting in Karaj River. Three scenarios were used. In the first scenario, meteorological data were used as input variables for model development. In the second, flow discharge data were used, while the third scenario used a combination of scenarios 1 and 2 as input variables for model development. For each scenario, different combinations of time series were considered. In the Wann and Wanfis models, the effective sub-time series components obtained by discrete wavelet transform were used as the new inputs to Ann and Anfis models to predict daily river flow time series. Wann was found to be the best technique for daily flow prediction throughout this study. The results indicate that the best Wann model (R2test = 0·993, RMSEtest = 0·004) outperformed the best Wanfis model (R2test = 0·976, RMSEtest = 0·008). The results show that only the input structures of scenarios 2 and 3 are efficient input structures for flow prediction in the study area. It was also shown that Wann models are capable of delivering an accurate flow prediction up to the next 4 d.

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