Abstract
ABSTRACT Rainfall-runoff modelling is at the core of any hydrological forecasting system. High spatio-temporal variability of precipitation patterns, complexity of the physical processes, and large quantity of parameters to characterize a watershed make the prediction of runoff rates quite difficult. In this study, a hyper-complex Artificial Neural Network (ANN) in the form of an Octonion-Valued Neural Network (OVNN) is proposed to estimate runoff rates. Evaluation of the proposed model is performed using a rainfall time series from a rain gauge near a Canadian watershed. Results of the AI-generated runoff rates illustrate its capacity to produce more computationally efficient runoff rates when compared to those obtained using a physically-based model. In addition, training the data using the proposed OVNN versus a real-valued neural network shows less space-complexity (1*3*1 vs. 8*10*8, respectively) and more accurate results (0.10% vs. 0.95%, respectively), that accounts for the efficiency of the OVNN model for real-time control applications.
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