The Use Support Vector Machine and Back Propagation Neural Network for Prediction of Daily Tidal Levels along the Jeddah Coast, Saudi Arabia

Sea level rise threatens to increase the impact of future 
storms and hurricanes on coastal communities. Accurate sea level 
change prediction and supplement is an important task in determining 
constructions and human activities in coastal and oceanic areas. In 
this study, support vector machines (SVM) is proposed to predict 
daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal 
parameter values of kernel function are determined using a genetic 
algorithm. The SVM results are compared with the field data and 
with back propagation (BP). Among the models, the SVM is superior 
to BPNN and has better generalization performance.

• E. A. Mlybari
• M. S. Elbisy
• A. H. Alshahri
• O. M. Albarakati

• Tides
• Prediction
• Support Vector Machines
• Genetic Algorithm
• Back-Propagation Neural Network
• Risk
• Hazards.

[1] G. H. Darwin, "On an apparatus for facilitating the reduction of tidal
observations,” Proc. R. Soc. (London) Ser. A 52, 1892, pp. 345–376.
[2] A. T. Doodson, 1958 "The analysis and predictions of tides in shallow
water,” Int. Hydrogr. Rev., Monaco, vol. 33, pp. 85–126, 1958.
[3] R. E. Kalman, 1960 "A new approach to linear filtering and prediction
problems” Trans. ASME, J. Basic Eng., vol. 82, no. 2, pp. 35–45, 1960.
[4] K. Mizumura, "Application of Kalman filtering to ocean data,” J
Waterway, Port, Coastal Ocean Eng., ASCE, vol. l10, no. 3, pp. 334–43,
1984.
[5] P. H.Yen, C. D.Jan, Y. P. Lee, and H. F. Lee, "Application of Kalman
filter to short-term tide level prediction,” Journal of Waterway; Port;
Coastal and Ocean Engineering; ASCE, vol. 122 no. 5, pp. 226–231,
1996.
[6] M. Vaziri, "Predicting Caspian Sea surface water level by ANN and
ARIMA models. Journal of Waterway, Port, Coastal and Ocean
Engineering, vol. 123, pp. 158–162, 1997.
[7] M. C. Deo, A. Jha, A. S. Chaphekar, and K. Ravicant "Neural networks
for wave forecasting,” Ocean Engineering, vol. 26, pp. 191–303, 2001.
[8] Tsai, C.P., and Lee, T.L., "Back-propagation neural network in tidal
level forecasting,” Journal of Waterway, Port, Coastal and Ocean
Engineering, ASCE, vol. 12, no.4, pp. 195–202, 1999.
[9] T. L. Lee, and D. S. Jeng, "Application of artificial neural networks in
tide forecasting,” Ocean Eng., vol. 29, no. 9, pp. 1003–1022, 2002.
[10] T. L. Lee, C. P. Tsai, D. S. Jeng, and R. J. Shieh, "Neural network for
prediction and supplement of tidal record in Taichung harbor,” Taiwan.
Adv. Eng. Softw. vol. 33, pp. 329–338, 2002.
[11] T. L. Lee, "Back-propagation neural network for long-term tidal
predictions,” Ocean Eng., vol. 31, pp. 225–238, 2004.
[12] C. Steidley, A. Sadovski, P. Tissot, R. Bachnak, and Z. Bowles, "Using
an artificial neural network to improve predictions of water levels where
tide charts fail,” Innovations in Applied Artificial Intelligence, vol. 35,
pp. 599–608, 2005.
[13] S. Rajasekaran, K. Thiruvenkatasamy and T. L. Lee, "Tidal level
forecasting using functional and sequential learning neural networks,”
Applied Mathematical Modelling, vol. 30, no. 1, pp. 85–103, 2005.
[14] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning
representations by back-propagation errors,” Nature, vol. 323, pp. 533-
536, 1986.
[15] M. S. El-Bisy, "Longshore current prediction with neural networks,” Ain
Shams Univ, Eng. Bulletin, vol. 40, no. 4, pp. 489-503, 2005.
[16] A. J. Smola, Learning with kernels. Ph.D. dissertation, GMD,
Birlinghoven, Germany, 1998.
[17] V. Cherkassky, and Y. Ma "Practical selection of SVM parameters and
noise estimation for SVM regression,” Neural Network, vol. 17, pp.113-
126, 2004.
[18] P. S. Yu, S. T. Chen, and I. F. Chang, "Support vector regression for
real-time flood stage forecasting,” J. Hydrology, vol. 328, pp. 704–716,
2006.
[19] U. Thissen, , R. Van Brakel, , A. P. De Weijer, , W. J. Melssen, and L
.M. C. Buydens, " Using support vector machines for time series
prediction,” Chemom. Intell. Lab. Syst., vol. 69, pp. 35–49, 2003.