Rainfall-runoff modelling using octonion-valued neural networks.

Shishegar, Shadab; Ghorbani, Reza; Saad Saoud, Lyes; Duchesne, Sophie ORCID: https://orcid.org/0000-0002-5619-0849 et Pelletier, Geneviève (2021). Rainfall-runoff modelling using octonion-valued neural networks. Hydrological Sciences Journal , vol. 66 , nº 13. pp. 1857-1865. DOI: 10.1080/02626667.2021.1962885.

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Résumé

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.

Type de document:	Article
Mots-clés libres:	machine learning; flow rate prediction; stormwater management; hydrology; multi-dimensional; hyper complex network
Centre:	Centre Eau Terre Environnement
Date de dépôt:	15 oct. 2021 17:26
Dernière modification:	07 août 2022 04:00
URI:	https://espace.inrs.ca/id/eprint/11955

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