Monthly total sediment forecasting using adaptive neuro fuzzy inference system

Firat M., Gungor M.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, cilt.24, sa.2, ss.259-270, 2010 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 2
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1007/s00477-009-0315-1
  • Dergi Adı: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.259-270
  • Anahtar Kelimeler: Monthly sediment, Total sediment forecasting, Great Menderes catchment, ANFIS, ANN, RAINFALL-RUNOFF MODEL, SUSPENDED SEDIMENT, NETWORK TECHNIQUES, PREDICTION, SIMULATION
  • İnönü Üniversitesi Adresli: Evet

Özet

Accurate forecasting of sediment is an important issue for reservoir design and water pollution control in rivers and reservoirs. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct monthly sediment forecasting system. To illustrate the applicability of ANFIS method the Great Menderes basin is chosen as the study area. The models with various input structures are constructed for the purpose of identification of the best structure. The performance of the ANFIS models in training and testing sets are compared with the observed data. To get more accurate evaluation of the results ANFIS models, the best fit model structures are also tested by artificial neural networks (ANN) and multiple linear regression (MLR) methods. The results of three methods are compared, and it is observed that the ANFIS is preferable and can be applied successfully because it provides high accuracy and reliability for forecasting of monthly total sediment.