Application of Recurrent Neural Networks for Streamflow Prediction Across a Mix Urban Rural Coastal Drainage System

Surface flow models provide a comprehensive assessment of rainfall-runoff processes to understand how a catchment system responds to different environmental conditions. Although, many modelling systems including conceptual and distributed physical hydrological approaches provide a concise expression of flow path heterogeneity and limited system response function at the catchment scale. Recently, deep leaning methods have been proposed for empirical rainfall-runoff modelling and to complement existing hydrological models (both distributed physical and conceptual approaches), particularly in a catchment where data to support process-based model is limited. This study implemented a new and innovative Deep Learning (DL) modelling system to simulate daily streamflow timeseries across a mix urban and rural coastal catchment in North Carolina, USA. We used a new generation of deep learning neural network (DLNN), i.e., Recurrent Neural Networks (RNNs), that seamlessly transformed data into intelligence and simulated sequential flow rates based on a set of collected flow factors. Furthermore, the architecture of RNNs provided a neural basis for efficient training procedure that had the ability to intelligently integrate any mathematical or logical algorithm into the simulation decision process. Analysis suggests that the effects of input data characteristics on model performance (sequential data), the uncertainty associated with forcing data, the amount of training data, and the correlation among different attributes of data series are important factors for a neural computational development underlying surface flow processes. RNN algorithm was able to learn the complexity of order or temporal dependence between observations, thereby it was capable of accurately model the complex multivariate sequences of a coastal rainfall-runoff process. Further investigation of RNN simulation revealed that while model architecture was important, training on a large amount of dataset was necessary to enforce spatio-temporal hierarchical relationships of data with the catchment attributes. Together our results provide an algorithmically informed simulation on the dynamics of daily streamflow simulation and maybe applicable to other complex catchment and climate settings.