Performance enhancement of a conceptual hydrological model by integrating artificial intelligence
| dc.contributor.author | Kumanlioglu A.A. | |
| dc.contributor.author | Fistikoglu O. | |
| dc.date.accessioned | 2025-04-10T11:07:05Z | |
| dc.date.available | 2025-04-10T11:07:05Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | A daily rainfall-runoff model has been improved by the integration of artificial neural network (ANN) and genetic algorithm (GA). The integrations are carried out on the daily rainfall-runoff model Génie rural à 4 paramètres journalier (GR4J). GR4J consists of production and routing storages. The production storage has only one process parameter and the routing storage has three. The ANN integration eliminates the three routing parameters. Automatic calibration capability has been added to the new hybrid model by integrating GA. The new hybrid model, which uses antecedent rainfall and temperature series, is applied to the Gediz River Basin in western Turkey. The results reveal that the hybrid model has better prediction performance than the original GR4J as well as the single ANN-based runoff prediction model. © 2019 American Society of Civil Engineers. | |
| dc.identifier.DOI-ID | 10.1061/(ASCE)HE.1943-5584.0001850 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/47156 | |
| dc.publisher | American Society of Civil Engineers (ASCE) | |
| dc.title | Performance enhancement of a conceptual hydrological model by integrating artificial intelligence | |
| dc.type | Article |