Application of a fuzzy inference system for the prediction of longshore sediment transport


Güner H. A. , Yumuk H. A.

APPLIED OCEAN RESEARCH, cilt.48, ss.162-175, 2014 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 48
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.apor.2014.08.008
  • Dergi Adı: APPLIED OCEAN RESEARCH
  • Sayfa Sayıları: ss.162-175

Özet

A fuzzy inference system (FIS) and a hybrid adaptive network-based fuzzy inference system (ANFIS), which combines a fuzzy inference system and a neural network, are used to predict and model longshore sediment transport (LST). The measurement data (field and experimental data) obtained from Kamphuis [1] and Smith et al. [2] were used to develop the model. The FIS and ANFIS models employ five inputs (breaking wave height, breaking wave angle, slope at the breaking point, peak wave period and median grain size) and one output (longshore sediment transport rate). The criteria used to measure the performances of the models include the bias, the root mean square error, the scatter index and the coefficients of determination and correlation. The results indicate that the ANFIS model is superior to the PIS model for predicting LST rates. To verify the ANFIS model, the model was applied to the Karaburun coastal region, which is located along the southwestern coast of the Black Sea. The LST rates obtained from the ANFIS model were compared with the field measurements, the CERC [3] formula, the Kamphuis [1] formula and the numerical model (LITPACK). The percentages of error between the measured rates and the calculated LST rates based on the ANFIS method, the CERC formula (K-sig = 0.39), the calibrated CERC formula (K-sig = 0.08), the Kamphuis [1] formula and the numerical model (LITPACK) are 6.5%, 413.9%, 6.9%, 15.3% and 18.1%, respectively. The comparison of the results suggests that the ANFIS model is superior to the FIS model for predicting LST rates and performs significantly better than the tested empirical formulas and the numerical model. (C) 2014 Elsevier Ltd. All rights reserved.