The utilization of a GR4J model and wavelet-based artificial neural network for rainfall-runoff modelling

Creative Commons License

Sezen C., Partal T.

WATER SUPPLY, cilt.19, sa.5, ss.1295-1304, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 5
  • Basım Tarihi: 2019
  • Doi Numarası: 10.2166/ws.2018.189
  • Dergi Adı: WATER SUPPLY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1295-1304
  • Anahtar Kelimeler: artificial neural network, conceptual, data-driven, GR4J, streamflow, wavelet, LOW-FLOW FORECASTS, HYDROLOGICAL MODELS, INTELLIGENCE MODELS, GENETIC ALGORITHM, DATA-DRIVEN, RIVER, DECOMPOSITION, INPUT, SKILL, SOIL
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Data-driven models and conceptual models have been utilized in an attempt to perform rainfall- runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall-runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Q(t-1)) and streamflow data the preceding two days (Q(t-2)) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.