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Anfis Model for Prediction of Current Velocity at Filyos Region

Year 2016, Volume: 8 Issue: 4, 1 - 9, 26.12.2016
https://doi.org/10.24107/ijeas.281420

Abstract

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.

References

  • Güner H. A. A., Yüksel Y., Çevik E. Ö., Estimation of wave parameters based on nearshore wind-wave correlations. Ocean Engineering, 63,52-62, 2013.
  • [2] Hasselmann, K., Barnett, T.P., Bouws, E., Carlson, H., Cartwright, D.E., Enke, K., Weing, J.A., Gienapp, H., Hasselmann, D.E., Krusemann, P., Meerburg, A., Muller, P., Olbers, K.J., Richter, K., Sell, W., Walden, W.H., Measurement of wind wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Deutsche Hydrograph, Zeit, Erganzung-self Reihe, A8, 1973.
  • [3] Shore Protection Manual (SPM), Coastal Engineering Research Center, Waterway Experiment Station, Corps of Engineers, Department of the Army, Vicksburg, MS, 1984.
  • [4] Coastal Engineering Manual (CEM), US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, 2000.
  • [5] Goda, Y., Revisiting Wilson’s formulas for simplified wind-wave prediction. Journal of Waterway, Port, Coastal and Ocean Engineering, 129, 93-95, 2003.
  • [6] Moeini M. H., Etemad-Shahidi A., Chegini V., 2010. Wave modelling and extreme value analysis off the northern coast of the Persian Gulf. Applied Ocean Research 32, 209-218.
  • [7] Justin Thomas Ta, G. S. Dwarakish, Numerical wave modelling–A review, International Conference On Water Resources, Coastal and Ocean Engineering (ICWRCOE 2015). Aquatic Procedia 4, 443 – 448, 2015.
  • [8] Horikawa K., Coastal engineering, An introduction to ocean engineering. University of Tokyo Press, Tokyo, Japan,1978.
  • [9] Zanganeh M., Yeganeh-Bakhtiary A., Yamashita T., ANFIS and ANN models for the estimation of wind and wave-induced current velocities at Joetsu-Ogata coast. Journal of Hydro informatics, 371-391, 2016.
  • [10] Kazeminezhad, M. H., Etemad-Shahidi, A., Mousavi, S. J., Application of fuzzy inference system in the prediction wave parameters. Ocean Engineering, 32, 1709-1725, 2005.
  • Özger, M., Şen, Z., Prediction of wave parameters by using fuzzy logic approach. Ocean Engineering, 34, 490-469, 2007.
  • [12] Günaydın K., The estimation of monthly mean significant wave heights by using artificial neural network and regression method. Ocean Engineering, 35, 1406-1415, 2008.
  • [13] Mahjoobi, J., Etemad-Shahidi, A., KAzeminezhad M. H., Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research, 30, 28-36, 2008.
  • [14] Bakhtyar, R., Yeganeh-Bakhtiary, A., Ghaheri, A., Application of neuro-fuzzy approach in prediction of run up in swash zone. Applied Ocean Research, 30, 17-27, 2008a.
  • [15] Bakhtyar, R., Ghaheri, A., Yeganeh-Bakhtiary, A., Baldock, T. E., Longshore sediment ransport estimation using fuzzy inference system. Applied Ocean Research, 30, 273-286, 2008b.
  • [16] Zanganeh, M., Mousavi, S. J., Etemad-Shadidi, An., Hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Engineering Application of Artificial Intelligence, 22, 1194-1202, 2009.
  • [17] Tür. R., Balas, C. E., Neuro-fuzzy approximation for prediction of significant wave heights: The case of Filyos Region. Journal of the Faculty of Engineering and Architecture of Gazi University, 25, 505-510, 2010.
  • [18] Aydoğan, A., Ayat, B., Öztürk, M. N., Çevik, E. Ö., Yüksel, Y., Current velocity forecasting in straits with artificial neural networks, a case study: Strait of İstanbul. Ocean Engineering, 37, 443-453, 2010.
  • [19] Shiri, J., Makarynskky, O., Dierickx, W., Far, A., Prediction of short-term operational water levels using an adaptive neuro-fuzzy inference system. Journal of Waterway, Port, Coastal and Ocean Engineering, 137, 344-354, 2011.
  • [20] Akpınar, A., Özger, M., Kömürcü, M. İ., Prediction of wave parameters by using fuzzy inference system and the parametric models along the south coasts of the Black Sea. Journal of Marine Science and Technology,19, 1–14, 2013.
  • Jang J. R., ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, mani and cybernetics, 23 (3), 1993.
  • [22] Patil S. G., Mandal S., Hegde A. V., Alavandar S., Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater. Ocean Engineering, 38, 186-196, 2011.
Year 2016, Volume: 8 Issue: 4, 1 - 9, 26.12.2016
https://doi.org/10.24107/ijeas.281420

Abstract

References

  • Güner H. A. A., Yüksel Y., Çevik E. Ö., Estimation of wave parameters based on nearshore wind-wave correlations. Ocean Engineering, 63,52-62, 2013.
  • [2] Hasselmann, K., Barnett, T.P., Bouws, E., Carlson, H., Cartwright, D.E., Enke, K., Weing, J.A., Gienapp, H., Hasselmann, D.E., Krusemann, P., Meerburg, A., Muller, P., Olbers, K.J., Richter, K., Sell, W., Walden, W.H., Measurement of wind wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Deutsche Hydrograph, Zeit, Erganzung-self Reihe, A8, 1973.
  • [3] Shore Protection Manual (SPM), Coastal Engineering Research Center, Waterway Experiment Station, Corps of Engineers, Department of the Army, Vicksburg, MS, 1984.
  • [4] Coastal Engineering Manual (CEM), US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, 2000.
  • [5] Goda, Y., Revisiting Wilson’s formulas for simplified wind-wave prediction. Journal of Waterway, Port, Coastal and Ocean Engineering, 129, 93-95, 2003.
  • [6] Moeini M. H., Etemad-Shahidi A., Chegini V., 2010. Wave modelling and extreme value analysis off the northern coast of the Persian Gulf. Applied Ocean Research 32, 209-218.
  • [7] Justin Thomas Ta, G. S. Dwarakish, Numerical wave modelling–A review, International Conference On Water Resources, Coastal and Ocean Engineering (ICWRCOE 2015). Aquatic Procedia 4, 443 – 448, 2015.
  • [8] Horikawa K., Coastal engineering, An introduction to ocean engineering. University of Tokyo Press, Tokyo, Japan,1978.
  • [9] Zanganeh M., Yeganeh-Bakhtiary A., Yamashita T., ANFIS and ANN models for the estimation of wind and wave-induced current velocities at Joetsu-Ogata coast. Journal of Hydro informatics, 371-391, 2016.
  • [10] Kazeminezhad, M. H., Etemad-Shahidi, A., Mousavi, S. J., Application of fuzzy inference system in the prediction wave parameters. Ocean Engineering, 32, 1709-1725, 2005.
  • Özger, M., Şen, Z., Prediction of wave parameters by using fuzzy logic approach. Ocean Engineering, 34, 490-469, 2007.
  • [12] Günaydın K., The estimation of monthly mean significant wave heights by using artificial neural network and regression method. Ocean Engineering, 35, 1406-1415, 2008.
  • [13] Mahjoobi, J., Etemad-Shahidi, A., KAzeminezhad M. H., Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research, 30, 28-36, 2008.
  • [14] Bakhtyar, R., Yeganeh-Bakhtiary, A., Ghaheri, A., Application of neuro-fuzzy approach in prediction of run up in swash zone. Applied Ocean Research, 30, 17-27, 2008a.
  • [15] Bakhtyar, R., Ghaheri, A., Yeganeh-Bakhtiary, A., Baldock, T. E., Longshore sediment ransport estimation using fuzzy inference system. Applied Ocean Research, 30, 273-286, 2008b.
  • [16] Zanganeh, M., Mousavi, S. J., Etemad-Shadidi, An., Hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Engineering Application of Artificial Intelligence, 22, 1194-1202, 2009.
  • [17] Tür. R., Balas, C. E., Neuro-fuzzy approximation for prediction of significant wave heights: The case of Filyos Region. Journal of the Faculty of Engineering and Architecture of Gazi University, 25, 505-510, 2010.
  • [18] Aydoğan, A., Ayat, B., Öztürk, M. N., Çevik, E. Ö., Yüksel, Y., Current velocity forecasting in straits with artificial neural networks, a case study: Strait of İstanbul. Ocean Engineering, 37, 443-453, 2010.
  • [19] Shiri, J., Makarynskky, O., Dierickx, W., Far, A., Prediction of short-term operational water levels using an adaptive neuro-fuzzy inference system. Journal of Waterway, Port, Coastal and Ocean Engineering, 137, 344-354, 2011.
  • [20] Akpınar, A., Özger, M., Kömürcü, M. İ., Prediction of wave parameters by using fuzzy inference system and the parametric models along the south coasts of the Black Sea. Journal of Marine Science and Technology,19, 1–14, 2013.
  • Jang J. R., ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, mani and cybernetics, 23 (3), 1993.
  • [22] Patil S. G., Mandal S., Hegde A. V., Alavandar S., Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater. Ocean Engineering, 38, 186-196, 2011.
There are 22 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Rıfat Tür

Dilayda Soylu

Publication Date December 26, 2016
Acceptance Date November 1, 2016
Published in Issue Year 2016 Volume: 8 Issue: 4

Cite

APA Tür, R., & Soylu, D. (2016). Anfis Model for Prediction of Current Velocity at Filyos Region. International Journal of Engineering and Applied Sciences, 8(4), 1-9. https://doi.org/10.24107/ijeas.281420
AMA Tür R, Soylu D. Anfis Model for Prediction of Current Velocity at Filyos Region. IJEAS. December 2016;8(4):1-9. doi:10.24107/ijeas.281420
Chicago Tür, Rıfat, and Dilayda Soylu. “Anfis Model for Prediction of Current Velocity at Filyos Region”. International Journal of Engineering and Applied Sciences 8, no. 4 (December 2016): 1-9. https://doi.org/10.24107/ijeas.281420.
EndNote Tür R, Soylu D (December 1, 2016) Anfis Model for Prediction of Current Velocity at Filyos Region. International Journal of Engineering and Applied Sciences 8 4 1–9.
IEEE R. Tür and D. Soylu, “Anfis Model for Prediction of Current Velocity at Filyos Region”, IJEAS, vol. 8, no. 4, pp. 1–9, 2016, doi: 10.24107/ijeas.281420.
ISNAD Tür, Rıfat - Soylu, Dilayda. “Anfis Model for Prediction of Current Velocity at Filyos Region”. International Journal of Engineering and Applied Sciences 8/4 (December 2016), 1-9. https://doi.org/10.24107/ijeas.281420.
JAMA Tür R, Soylu D. Anfis Model for Prediction of Current Velocity at Filyos Region. IJEAS. 2016;8:1–9.
MLA Tür, Rıfat and Dilayda Soylu. “Anfis Model for Prediction of Current Velocity at Filyos Region”. International Journal of Engineering and Applied Sciences, vol. 8, no. 4, 2016, pp. 1-9, doi:10.24107/ijeas.281420.
Vancouver Tür R, Soylu D. Anfis Model for Prediction of Current Velocity at Filyos Region. IJEAS. 2016;8(4):1-9.

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