Performance enhancement of a conceptual hydrological model by integrating artificial intelligence

KUMANLIOĞLU A. A., FISTIKOĞLU O.

Journal of Hydrologic Engineering, vol.24, no.11, 2019 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 11
  • Publication Date: 2019
  • Doi Number: 10.1061/(asce)he.1943-5584.0001850
  • Journal Name: Journal of Hydrologic Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Artificial neural networks, Conceptual hydrological model, Gediz River Basin, Genetic algorithms, Génie rural à 4 paramètres journalier (gr4j), Hybrid hydrological model
  • Manisa Celal Bayar University Affiliated: Yes

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.