Parametric and nonparametric regression models in study of the length of hydraulic jump after a multi-segment sharp-crested V-notch weir

Saadatnejadgharahassanlou H., Zeynali R. I., Vaheddoost B., Gharehbaghi A.

WATER SUPPLY, cilt.20, sa.3, ss.809-818, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.2166/ws.2019.198
  • Dergi Adı: WATER SUPPLY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Geobase, ICONDA Bibliographic, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.809-818
  • Anahtar Kelimeler: experimental model, length of hydraulic jump, multi-segment sharp crested V-notch weir, open channel flow, parametric and nonparametric model, FLOW, ENERGY, FORCE
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

A multi-segment sharp-crested V-notch weir (SCVW) was used both theoretically and experimentally in this study to evaluate the length of the hydraulic jump at the downstream of the weir. For this aim, a SCVW with three triangular segments at different tail-water depths (tailgate angles), and ten different discharges at a steady flow condition were investigated. Then, the most effective parameters on the length of the hydraulic jump are defined and several parametric and nonparametric regression models, namely multi-linear regression (MLR), additive non-linear regression (ANLR), multiplicative non-linear regression (MNLR), and generalized regression neural network (GRNN) models are compared with two semi-empirical regression models from the literature. The results indicate that the GRNN model is the best model among the selected models. These results are also linked to the nature of the hydraulic jump and the turbulent behavior of the phenomenon, which masks the experimental results with outliers.