Current velocity plays a significant role in coastal engineering,
especially coastal sedimentation, coastal pollution transmission, and design of
coastal structures. Moreover, it is great important to determine coastal
pollution propagation in time and the area affected by pollution transmission.
Because of these reasons, current velocity is predicted based on observed data
in this study. Current velocity data which are measured for 2 hours during 2
years in Filyos Region are utilized to
develop several Adaptive Neuro-Fuzzy Inference System (ANFIS) models on Matlab
to estimate future current velocity. After prediction of two hourly averages of
current velocities from previous values by ANFIS model, the predicted data is
compared with the actual one measured in the field. Therefore, statistical
parameters in literature including root mean square error (RMSE), mean absolute
error (MAE), and correlation coefficient (R) are used to test acceptability of
proposed ANFIS models. The study results indicate that proposed models provide
better results in comparison to widespread stochastic approaches. Consequently,
this study is an alternative to other prediction methods considering the aims
of current velocity prediction mentioned above.
Subjects | Engineering |
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Journal Section | Articles |
Authors | |
Publication Date | December 26, 2016 |
Acceptance Date | November 1, 2016 |
Published in Issue | Year 2016 Volume: 8 Issue: 4 |