Depot Institutionnel de l'UMBB >
Publications Scientifiques >
Publications Internationales >
Veuillez utiliser cette adresse pour citer ce document :
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7208
|
Titre: | Rainfall–runoff modelling using octonion-valued neural networks |
Auteur(s): | Shishegar, Shadab Ghorbani, Reza Saad Saoud, Lyes Duchesne, Sophie Pelletier, Geneviève |
Mots-clés: | Machine learning Flow rate prediction Stormwater management Hydrology Multidimensional Hyper complex network |
Date de publication: | 2021 |
Editeur: | Taylor & Francis |
Collection/Numéro: | Hydrological Sciences Journal/ (2021) |
Résumé: | Rainfall–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 applications |
URI/URL: | https://doi.org/10.1080/02626667.2021.1962885 https://www.tandfonline.com/doi/abs/10.1080/02626667.2021.1962885?journalCode=thsj20 http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7208 |
ISSN: | 02626667 2150-3435 Electronic |
Collection(s) : | Publications Internationales
|
Fichier(s) constituant ce document :
Il n'y a pas de fichiers associés à ce document.
|
Tous les documents dans DSpace sont protégés par copyright, avec tous droits réservés.
|