Shishegar, ShadabGhorbani, RezaSaad Saoud, LyesDuchesne, SophiePelletier, Geneviève2021-10-122021-10-122021026266672150-3435 Electronichttps://doi.org/10.1080/02626667.2021.1962885https://www.tandfonline.com/doi/abs/10.1080/02626667.2021.1962885?journalCode=thsj20https://dspace.univ-boumerdes.dz/handle/123456789/7208Rainfall–runoff modelling is at the core of any hydrological forecasting system. The high spatio-temporal variability of precipitation patterns, complexity of the physical processes, and large quantity of parameters required to characterize a watershed make the prediction of runoff rates quite difficult. In this study, a hyper-complex artificial neural network 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 raingauge near a Canadian watershed. Results of the artificial intelligence-generated runoff rates illustrate its capacity to produce more computationally efficient runoff rates compared to those obtained using a physically based model. In addition, training the data using the proposed OVNN vs. 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), which accounts for the efficiency of the OVNN model for real-time control applicationsenMachine learningFlow rate predictionStormwater managementHydrologyMultidimensionalHyper complex networkArticle