Rainfall–runoff modelling using octonion-valued neural networks

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