Streamflow forecasting for the Hunza river basin using ANN, RNN, and ANFIS models

This research study investigates the use of artificial neural network (ANN), recurrent neural network (RNN), and ANFIS for monthly streamflow forecasting in the Hunza river basin of Pakistan. Different models were developed using precipitation, temperature, and discharge data. Two statistical performance indicators, i.e. RMSE and R-2, were used to assess the performance of machine learning techniques. Based on these performance indicators, the ANN model predicts monthly streamflow more accurately than the RNN and ANFIS models. To assess the performance of the ANN model, three architectures were used, namely 2-1-1, 2-2-1, and 2-3-1. The ANN architecture with a 2-3-1 configuration had higher R-2 values of 0.9522 and 0.96998 for the training and testing phases, respectively. For each RNN architecture, three transfer functions were used, namely Tan-sig, Log-sig, and Purelin. The architecture with a 2-1-1 configuration based on tan-sig transfer function performed well in terms of R-2 values, which were 0.7838 and 0.8439 for the training and testing phases, respectively. For the ANFIS model, the R-2 values were 0.7023 and 0.7538 for both the training and testing phases, respectively. Overall, the findings suggest that the ANN model with a 2-3-1 architecture is the most effective for predicting monthly streamflow in the Hunza river basin.