Prediction of maximum annual flood discharges using artificial neural network approaches

ANILAN T., NACAR S., KANKAL M., YÜKSEK Ö.

GRADEVINAR, cilt.72, sa.3, ss.215-224, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 72 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.14256/jce.2316.2018
  • Dergi Adı: GRADEVINAR
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, ICONDA Bibliographic, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.215-224
  • Anahtar Kelimeler: artificial neural networks, principal component analysis, maximum annual flows, PRINCIPAL COMPONENT ANALYSIS, L-MOMENTS APPROACH, FREQUENCY-ANALYSIS, INDEX-FLOOD, FEEDFORWARD NETWORKS, STREAMFLOW, BASIN, CLASSIFICATION, RAINFALL, QUALITY
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_ NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows.