Performance Enhancement of a Conceptual Hydrological Model by Integrating Artificial Intelligence

KUMANLIOĞLU A. A., FISTIKOĞLU O.

JOURNAL OF HYDROLOGIC ENGINEERING, cilt.24, sa.11, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 11
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1061/(asce)he.1943-5584.0001850
  • Dergi Adı: JOURNAL OF HYDROLOGIC ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Conceptual hydrological model, Genie rural a 4 parametres journalier (GR4J), Artificial neural networks, Genetic algorithms, Hybrid hydrological model, Gediz River Basin, RAINFALL-RUNOFF MODEL, NEURAL-NETWORK, CALIBRATION, CATCHMENT, ANN, DISCHARGE
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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 Genie rural a 4 parametres 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.