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Streamflow prediction is a significant undertaking for water resources planning and management. Accurate forecasting of streamflow always being a challenging task for the hydrologist due to its high stochasticity and dynamic patterns. Several traditional and the deep learning models have been applied to simulate the complex nature of the hydrological system. However, to develop and explore a better expert system for prediction is a continuous exertion for hydrological studies. In this study, a deep neural network, namely a one-dimensional convolutional neural network (1D-CNN) and extreme learning machine (ELM) are explored for one-step-ahead streamflow forecasting for three-time horizons (daily, weekly and monthly) in Gilgit River, Pakistan. The 1D-CNN model gained incredible popularity due to its state-of-the-art performance and nominal computational complexity; while ELM model performed superfast …