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The choice between Radial Basis function and Feed Forward Neural Network to predict long term tidal condition
Hengameh Motamedi1, Maryam Rahbani *1, Abbas Harifi1, Danial Ghaderi1
1- University of Hormozgan
Abstract:   (486 Views)
Possessing precise water level data in any coastal area is crucial, for any coastal engineering or managements. One of the main processes responsible for a regular water level changes is tide. Due to its nature, tidal prediction is relatively easily accessible. However, the precision of the results depends on the number of constituents have been considered for the prediction. The aim of this paper is to identify the most relevant tidal constituents and their relevant amplitudes for tidal prediction in the Beris port, south of Iran, using artificial neural network (ANN). The main constituents in the area is obtained as M_2, K_1, S_2, N_2 and O_1. To regenerate the tidal condition considering these constituents two ANN methods has been applied including Feed Forward, and Radial Basis Functions (RBF). For the training and network test tidal data of the year 2017 has been considered. For the training a variety of months and constructions has been applied. For the Feed-Forward the Levenberg-Marquardt learning method has been considered. After executing different structures in terms of the number of neurons in the hidden layer and taking into account the minimum error and run time, the network with 5 neurons in the hidden layer and two months training was qualified. For the RBF, the radius of 2.5 has been qualified. The evaluated network of both Feed-Forward and RBF has been employed to reproduce tidal water level of the whole year 2018, and the results were compared with both field data and those derived from harmonic analysis. It was found that the three layers Feed-Forward network shows the best performance in tidal prediction with the correlation coefficient of 0.85, which is followed by RBF, with the correlation coefficient of 0.81.
Keywords: Feed Forward Neural Network|Radial Basis Functions|Tidal prediction|Beris Port ,
Full-Text [PDF 1024 kb]   (52 Downloads)    
Type of Study: Research | Subject: Coastal Engineering
Received: 2020/09/2 | Accepted: 2020/10/18
References
1. W. Wang and H. Yuan, "A Tidal Level Prediction Approach Based on BP Neural Network and Cubic B-Spline Curve with Knot Insertion Algorithm," Mathematical Problems in Engineering, vol. 2018, 2018. [DOI:10.1155/2018/9835079]
2. D. Pugh and P. Woodworth, Sea-level science: understanding tides, surges, tsunamis and mean sea-level changes. Cambridge University Press, 2014. [DOI:10.1017/CBO9781139235778]
3. M. Guan et al., "A method of establishing an instantaneous water level model for tide correction," Ocean Engineering, vol. 171, pp. 324-331, 2019. [DOI:10.1016/j.oceaneng.2018.11.016]
4. S. Cai, L. Liu, and G. Wang, "Short-term tidal level prediction using normal time-frequency transform," Ocean Engineering, vol. 156, pp. 489-499, 2018. [DOI:10.1016/j.oceaneng.2018.03.021]
5. G. W. Platzman, "Ocean tides and related waves," Mathematical problems in the geophysical sciences, vol. 14, no. Part 2, pp. 239-291, 1971.
6. B. Hong et al., "Potential physical impacts of sea-level rise on the Pearl River Estuary, China," Journal of Marine Systems, vol. 201, p. 103245, 2020. [DOI:10.1016/j.jmarsys.2019.103245]
7. Y. H. Song, Q. X. Xuan, and A. T. Johns, "Comparison studies of five neural network based fault classifiers for complex transmission lines," Electric power systems research, vol. 43, no. 2, pp. 125-132, 1997. [DOI:10.1016/S0378-7796(97)01168-1]
8. B. B. Parker, "Tidal analysis and prediction.," 2007.
9. E. A. Fanjul, B. P. Gómez, and I. R. Sánchez-Arévalo, "A description of the tides in the Eastern North Atlantic," Progress in Oceanography, vol. 40, no. 1, pp. 217-244, 1997. [DOI:10.1016/S0079-6611(98)00003-2]
10. T.-L. Lee, "Back-propagation neural network for long-term tidal predictions," Ocean Engineering, vol. 31, no. 2, pp. 225-238, 2004. [DOI:10.1016/S0029-8018(03)00115-X]
11. T.-L. Lee, O. Makarynskyy, and C.-C. Shao, "A combined harmonic analysis--artificial neural network methodology for tidal predictions," Journal of Coastal Research, vol. 23, no. 3 (233), pp. 764-770, 2007. [DOI:10.2112/05-0492.1]
12. O. Okwuashi and D. N. Olayinka, "Tide modelling using the Kalman filter," Journal of Spatial Science, vol. 62, no. 2, pp. 353-365, 2017. [DOI:10.1080/14498596.2016.1245162]
13. O. Okwuashi, C. Ndehedehe, and H. Attai, "Tide modeling using partial least squares regression," Ocean Dynamics, vol. 70, no. 8, pp. 1089-1101, 2020. [DOI:10.1007/s10236-020-01385-1]
14. G. Li, Y. Hao, and Y. Zhao, "Research of neural network to tidal prediction," in 2009 International Joint Conference on Computational Sciences and Optimization, 2009, vol. 2, pp. 282-284. [DOI:10.1109/CSO.2009.347]
15. F. A. Madah, "The amplitudes and phases of tidal constituents from Harmonic Analysis at two stations in the Gulf of Aden," Earth Systems and Environment, vol. 4, no. 2, pp. 321-328, 2020. [DOI:10.1007/s41748-020-00152-y]
16. R. O. Strobl and F. Forte, "Artificial neural network exploration of the influential factors in drainage network derivation," Hydrological Processes: An International Journal, vol. 21, no. 22, pp. 2965-2978, 2007. [DOI:10.1002/hyp.6506]
17. R. Özçelik, M. J. Diamantopoulou, J. R. Brooks, and H. V Wiant Jr, "Estimating tree bole volume using artificial neural network models for four species in Turkey," Journal of environmental management, vol. 91, no. 3, pp. 742-753, 2010. [DOI:10.1016/j.jenvman.2009.10.002]
18. J. A. Anderson, An introduction to neural networks. MIT press, 1995. [DOI:10.7551/mitpress/3905.001.0001]
19. A. M. Salim, G. S. Dwarakish, K. V Liju, J. Thomas, G. Devi, and R. Rajeesh, "Weekly prediction of tides using neural networks," Procedia Engineering, vol. 116, no. 1, pp. 678-682, 2015. [DOI:10.1016/j.proeng.2015.08.351]
20. B. L. Meena and J. D. Agrawal, "Tidal level forecasting using ANN," Procedia Engineering, vol. 116, pp. 607-614, 2015. [DOI:10.1016/j.proeng.2015.08.332]
21. L. Pashova and S. Popova, "Daily sea level forecast at tide gauge Burgas, Bulgaria using artificial neural networks," Journal of Sea Research, vol. 66, no. 2, pp. 154-161, 2011. [DOI:10.1016/j.seares.2011.05.012]
22. M. Janati, M. Kolahdoozan, and H. Imanian, "Artificial Neural Network Modeling for the Management of Oil Slick Transport in the Marine Environments," Pollution, vol. 6, no. 2, pp. 399-415, 2020.
23. S.-W. Kim, A. Lee, and J. Mun, "A Surrogate Modeling for Storm Surge Prediction Using an Artificial Neural Network," Journal of Coastal Research, no. 85, pp. 866-870, 2018. [DOI:10.2112/SI85-174.1]
24. W. J. Palm, Introduction to MATLAB 7 for Engineers, vol. 7. McGraw-Hill New York, 2005.
25. A. J. Adeloye and A. De Munari, "Artificial neural network based generalized storage--yield--reliability models using the Levenberg--Marquardt algorithm," Journal of Hydrology, vol. 326, no. 1-4, pp. 215-230, 2006. [DOI:10.1016/j.jhydrol.2005.10.033]
26. M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE transactions on Neural Networks, vol. 5, no. 6, pp. 989-993, 1994. [DOI:10.1109/72.329697]
27. R. Battiti, "First-and second-order methods for learning: between steepest descent and Newton's method," Neural computation, vol. 4, no. 2, pp. 141-166, 1992. [DOI:10.1162/neco.1992.4.2.141]
28. Z.-G. Zhang, J.-C. Yin, and C. Liu, "A modular real-time tidal prediction model based on Grey-GMDH neural network," Applied Artificial Intelligence, vol. 32, no. 2, pp. 165-185, 2018. [DOI:10.1080/08839514.2018.1451220]
29. M. H. Beale, M. T. Hagan, and H. B. Demuth, "Neural Network ToolboxTM, User's Guide, MATLAB®R2015a, The MathWorks," Inc., Natick, MA, USA, vol. 410, 2015.
30. S. E. Vt and Y. C. Shin, "Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems," IEEE transactions on neural networks, vol. 5, no. 4, pp. 594-603, 1994. [DOI:10.1109/72.298229]
31. J. Yin, Z. Zou, and F. Xu, "Sequential learning radial basis function network for real-time tidal level predictions," Ocean engineering, vol. 57, pp. 49-55, 2013. [DOI:10.1016/j.oceaneng.2012.08.012]
32. S. Ardani and M. Soltanpour, "Modelling of sediment transport in Beris fishery port," Civil Engineering Infrastructures Journal, vol. 48, no. 1, pp. 69-82, 2015.
33. D. Ghaderi and M. Rahbani, "Detecting shoreline change employing remote sensing images (Case study: Beris Port-east of Chabahar, Iran)," International Journal of Coastal and Offshore Engineering, vol. 3, pp. 1-8, 2020.
34. M. Sayehbani and D. Ghaderi, "Numerical Modeling of Wave and Current Patterns of Beris Port in East of Chabahar-Iran," International Journal of Coastal and Offshore Engineering, vol. 3, no. 1, pp. 21-29, 2019.
35. C. Amante and B. W. Eakins, "ETOPO1 arc-minute global relief model: procedures, data sources and analysis," 2009.
36. United States Geological Survey, "EarthExplorer," 2020. https://earthexplorer.usgs.gov/ (accessed Aug. 02, 2020).
37. A. Zhang, J. Yin, J. Hu, and C. Yu, "Modular tidal level short-term forecasting based on BP neural networks," in Proceedings of the 33rd Chinese Control Conference, 2014, pp. 5037-5042. [DOI:10.1109/ChiCC.2014.6895796]
38. M. G. Foreman, "G, 1977: Manual for tidal heights analysis and prediction," Pac. Mar. Sci. Rep, vol. 77, no. 10, 1977.
39. G. Dietrich and K. Kalle, "General oceanography; an introduction," 1957.
40. M. Mahmoudof and M. Bagheri, "Determination of Compound and Overtide Constituents near the Eastern Iranian Coast of Makran," Journal of Oceanography, vol. 10, no. 37, pp. 33-41, 2019, doi: 10.29252/joc.2019.10.12162. [DOI:10.29252/JOC.2019.10.12162]
41. K. Hornik, M. Stinchcombe, H. White, and others, "Multilayer feedforward networks are universal approximators.," Neural networks, vol. 2, no. 5, pp. 359-366, 1989. [DOI:10.1016/0893-6080(89)90020-8]
42. E. Sertel, H. K. Cigizoglu, and D. U. Sanli, "Estimating daily mean sea level heights using artificial neural networks," Journal of Coastal Research, vol. 24, no. 3 (243), pp. 727-734, 2008. [DOI:10.2112/06-742.1]
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