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Review on Applications of Neural Network in Coastal Engineering

G. S. Dwarakish, Shetty Rakshith, Usha Natesan


Artificial Neural Networks (ANN) finds wide variety of application in solving problems related to coastal engineering. Its ability to learn highly complex interrelationship based on provided data sets with the help of a learning algorithm along with built in error tolerance and less amount of data requirement, makes it a powerful modeling tool in the research community. Large number of studies has been carried out in various fields like prediction of wave parameters, tidal level and storm surge, estimation of design parameters, liquefaction depth and scour depth to name a few. Various forecasting, estimation and supplement to the missing data studies carried out from different perspective ranging from, the sensitivity analysis to check the effect of input parameters and reduce the input size by discarding less effective ones; reducing the input size by using data assimilation techniques like principal component analysis to decrease the computational time requirement; usage of updated algorithms to overcome the problem of overfitting and overlearning, thereby increasing the network efficiency; has been carried out successfully, establishing ANN as an strong alternative to the data demanding and time consuming hydrodynamic and numerical models. As the validity of ANN to the ocean engineering applications became increasingly evident studies were incorporated in practical applications as well. Studies are being carried out to merge ANN with other AI techniques of Genetic Programming and Fuzzy Logic approaches to overcome the setbacks observed in ANN models. The studies have successfully shown that ANN can be applied to solve vast problems related to ocean engineering problems by meticulous selection of data, input parameters, network architecture and learning algorithms.


Artificial Neural Networks, Artificial Intelligence, Coastal Engineering, Ocean Engineering.

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Mase, H., Sakamoto, M. & Sakai, T. 1995. Neural network for stability analysis of rubble mound breakwater. Journal of Waterway, Port, Coastal, and Ocean Engineering, 121, 294-299.

The ASCE Task Committee. 2000. Journal of Hydrologic Engineering, Vol.5 (2). ASCE. 115-136.

Haykin, S. 2006. Neural Networks- A comprehensive foundation, New Delhi, Prentice Hall of India Private Limited.

Vaziri, M. 1997. Predicting Caspian Sea surface water level by ANN and ARIMA models. Journal of Waterway, Port, Coastal and, Ocean Engineering, 123, 158-162.

Tsai, C. P. & Lee, T. L. 1999. Back propogation neural network in tidal level forecasting. Journal of Waterway, Port, Coastal and, Ocean Engineering, 125, 195-202.

Lee, T. L. & Jeng, D. S. 2002. Application of artificial neural networks in tide forecasting. Ocean Engineering, 29, 1003-1022.

Lee, T. L., Tsai, C. P., Jeng, D. S. & Shieh, R. J. 2002. Neural network for the prediction and supplement of tidal record in Taichung Harbor, Taiwan. Advances in Engineering Software, 33, 329-338.

Lee, T. L. 2004. Back propagation neural network for long-term tidal prediction. Ocean Engineering, 31, 225-238.

Huang, W., Murray, C., Kraus, N. & Rosati, J. 2003. Development of regional neural network for prediction of coastal water level predictions. Ocean Engineering, 30, 2275-2295.

Vivekanadan, N. & Singh, C. B. 2002. Prediction of tides using hydrodynamic and neural network approach. Indian Journal of Marine Science 32, 25-30.

Chen, B. F., Wang, H. D. & Chu, C. C. 2007. Wavelet and ANN analyses of tide forecasting and supplement of tides around Taiwan and South China Sea. Ocean Engineering, 34, 2161-2175.

Filippo, A., Rebelo Torres, A., Kjerfve, B. & Monat, A. 2012. Application of Artificial Neural Network (ANN) to improve forecasting of sea level. Ocean & Coastal Management, 55, 101-110.

Deo, M. C. & Naidu, C. S. 1999. Real time wave forecasting using neural networks. Ocean Engineering, 26, 191-203.

Deo, M. C., Jha, A., Chaphekar, A. S. & Ravikant, K. 2001. Neural networks for wave forecasting. Ocean Engineering, 28, 889-898.

Deo, M. C. & Jagdale, S. S. 2003. Prediction of breaking waves with neural networks. Ocean Engineering, 30, 1163-1178.

Balas, C. E., Koc, L. & Balas, L. 2004. Predictions of missing wave data by recurrent neuronets. Journal of Waterway, Port, Coastal and, Ocean Engineering. 130, 256-265.

Rao, S. & Mandal, S. 2005. Hindcasting of storm waves using neural networks. Ocean Engineering, 32, 667-684.

Londhe, S. N. & Panchang, V. 2006. One-day wave forecasts based on artificial neural networks. Journal of Atmospheric and Oceanic Technology, 23, 1593-1603.

Mahjoobi, J., Etemad-Shahidi, A. & Kazeminezhad, M. H. 2008. Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research, 30, 28-36.

Charhate, S. B., Deo, M. C. & Londhe, S. N. 2008. Inverse modeling to derive wind parameters from wave measurements. Applied Ocean Research, 30, 120-129.

Londhe, S. N. 2008. Soft computing approach for real-time estimation of missing wave heights. Ocean Engineering, 35, 1080-1089.

Kamranzad, B., Etemad-Shahidi, A. & Kazeminezhad, M. H. 2011. Wave height forecasting in Dayyer, the Persian Gulf. Ocean Engineering, 38, 248-255.

Van Der Meer, J. W.1988. Rock slopes and gravel beaches under wave attack. PhD, Delft University of Technology.

Smith, W. G., Kobayashi, N. & Kaku, S. 1992. Profile changes of rock slopes by irregular waves. In: Proceedings of 23rd International Conference on Coastal Engineering, New York. ASCE, 1559-1572.

Yagci, O., Mercan, D. E., Cigizoglu, H. K. & Kabdasli, M. S. 2005. Artificial intelligence methods in breakwater damage ratio estimation. Ocean Engineering, 32, 2088-2106.

Panizzo, A. & Briganti, R. 2007. Analysis of wave transmission behind low-crested breakwaters using neural networks. Coastal Engineering, 54, 643-656.

Van Der Meer, J. W., Briganti, R., Zanuttigh, B. & Baoxing, W. 2005. Wave transmission at low crested structures. Coastal Engineering, 52 915–929.

Lee, T. L., Jeng, D. S., Zhang, G. H. & Hong, J. H. 2007. Neural network modeling for estimation of scour depth around bridge piers. Journal of Hydrodynamics, 19, 378-386.

Lotfollahi-Yaghin, M. A. & Sanaaty, B. 2008. A new method in determining random wave-induced inline forces. World Applied Sciences Journal, 3, 674-683.

Balas, C. E., Koç, M. L. & Tür, R. 2010. Artificial neural networks based on principal component analysis, fuzzy systems and fuzzy neural networks for preliminary design of rubble mound breakwaters. Applied Ocean Research, 32, 425-433.

Kankal, M. & Yüksek, Ö. 2012. Artificial neural network approach for assessing harbor tranquility: The case of Trabzon Yacht Harbor, Turkey. Applied Ocean Research, 38, 23-31.

Chang, H. K. & Chien, W. A. 2006. Neural network with multi-trend simulating transfer function for forecasting typhoon wave. Advances in Engineering Software, 37, 184-194.

Lee, T. L. 2008. Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan. Engineering Applications of Artificial Intelligence, 21, 63-72.

Hsu, T. W., Liao, C. M. & Lee, Z. X. 1999. Finite element method to calculate the surge deviation for north-east coastal of Taiwan. Journal of Chinese Institute Civil and Engineering, 11, 849-857.

Lee, T. L. 2009. Predictions of typhoon storm surge in Taiwan using artificial neural networks. Advances in Engineering Software, 40, 1200-1206.

Mase, H., Yasuda, T. & Mori, N. 2011. Real-time prediction of tsunami magnitudes in Osaka Bay, Japan, using an artificial neural network. Journal of Waterway, Port, Coastal, and Ocean Engineering, 137, 263-268.

Okumura, Y., Takahashi, T., Suzuki, S. & Kawata, Y. 2003. Effects of nonuniformity of wave source due to asperity. Miyagi, Japan: Tohoku University.

Kawata, Y., Okumura, Y., Takahashi, T. & Suzuki, S. 2003. Estimation of an effect of the heterogeneous tsunami source of the Nankai earthquake due to asperities Proceedings of Coastal Engineering, Japan Society of Civil Engineers (JSCE), 50, 306–310 (in Japanese).

Jeng, Cha, D. S., Blumenstein, D. & Michael. 2004. Neural network model for the prediction of wave-induced liquefaction potential. Ocean Engineering, 31, 2073-2086.

Zhang, H., Jeng, D. S., Cha, D. & Blumenstein, M. 2007. Parametric study on the prediction of wave-induced liquefaction using an artificial neural network model. Journal of Coastal Research, 374-378.

Bhattacharya, B., & Solomatine, D., P. 2006.Machine learning in sedimentation modelling. Neural Networks,19, 208-214.

Gangfeng, Ma. & Shuguang, Liu. 2003. A forecasting model of delta evolution using artificial neural networks. International Conference on Estuaries and Coasts, November 9-11, Hangzhou, China.

Pape , L., Ruessink, B. G., Wiering, A. M., and Turner, I. L. 2007. Recurrent neural network modelling of nearshore sandbar behaviour. Neural Networks, 20, 509-518.

Hashemi, M. R., Ghadampour, Z. & Neill, S. P. 2010. Using an artificial neural network to model seasonal changes in beach profiles. Ocean Engineering, 37, 1345-1356.

Goncalves, R. M., Awange, J. L., Krueger, C. P., Heck, B. & Coelho, L. D. S. 2012. A comparison between three short-term shoreline prediction models. Ocean & Coastal Management, 69, 102-110.

Deo, M. C. 2010. Artificial neural networks in coastal and ocean engineering. Indian Journal of Geo-Marine Science, 39, 589-596.


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