Abstract
Prediction of reflection coefficient (Kr) for emerged perforated semicircular breakwater (EPSBW) using artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) is carried out in the present paper. A new approach has been adopted in the present work using ANN and ANFIS models for the prediction of the reflection coefficient (Kr) for the wave periods beyond the range of the dataset used for training the network. The experimental data obtained for a scaled down EPSBW model from regular wave flume experiments at Marine Structure laboratory of National Institute of Technology Karnataka, Surathkal, Mangaluru, India was used. The ensemble was segregated such that certain higher ranges of wave periods were excluded in the training, and possibility of prediction was checked. The independent input parameters (Hi, T, S, D, R, d, hs) that influence the reflection coefficient (Kr) are considered for training as well as testing, where Hi is the incident wave height, T is the wave period, S is the spacing of perforations, D is the diameter of the perforations, R is the radius of the breakwater, d is the depth of the water and hs is the structure height. The accuracy of predictions of reflection coefficient (Kr) is done based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The study shows that ANN and ANFIS models may be used for prediction of reflection coefficient Kr of semicircular breakwater for beyond the data range of wave periods used for training. However, ANFIS outperformed ANN model in the prediction of Kr in the case of beyond the data range segregation method.
Keywords
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Sundar V, Ragu V (1998) Dynamic pressures and run-up on semicircular breakwaters due to random waves. Ocean Eng 25(2–3):221–241. https://doi.org/10.1016/S0029-8018(97)00007-3
Dhinakaran G, Sundar V, Sundaravadivelu R (2002) Dynamic pressures and forces exerted on impermeable and seaside perforated semicircular breakwaters due to regular waves. Ocean Eng 29:1981–2004. https://doi.org/10.1016/S0029-8018(01)00106-8
Yuan D, Tao J (2003) Wave forces on submerged, alternately submerged, and emerged semicircular breakwaters. Coast Eng 48:75–93. https://doi.org/10.1016/S0378-3839(02)00169-2
Dhinakaran G, Sundar V, Sundaravadivelu R, Graw KU (2009) Effect of perforations and rubble mound height on wave transformation characteristics of surface piercing semicircular breakwaters. Ocean Eng (Elsevier) 36(15–16):1182–1198. https://doi.org/10.1016/j.oceaneng.2009.08.005
Young DM, Testik FY (2011) Wave reflection by submerged vertical and semicircular breakwaters. Ocean Eng (Elsevier) 38(10):1269–1276. https://doi.org/10.1016/j.oceaneng.2011.05.003
Kudumula SR, Mutukuru MRG (2013) Experimental studies on low crested rubble mound, semicircular breakwaters and vertical wall system. 4(3):213–226. http://journals.sagepub.com/doi/pdf/10.1260/1759-3131.4.3.213
Yagci O, Mercan DE, Cigizoglu HK, Kabdasli MS (2005) Artificial intelligence methods in breakwater damage ratio estimation. Ocean Eng 32(17–18):2088–2106. https://doi.org/10.1016/j.oceaneng.2005.03.004
Erdik T (2009) Fuzzy logic approach to conventional rubble mound structures design. Expert Syst Appl (Elsevier Ltd.) 36(3):4162–4170. https://doi.org/10.1016/j.eswa.2008.06.012
Mandal S, Patil SG, Hegde AV (2009) Wave transmission prediction of multilayer floating breakwater using neural network, International conference in Ocean Engineering (ICOE 2009). IIT Madras, Chennai, India
Deo MC (2010) Artificial neural networks in coastal and ocean engineering. Indian J Geo-Marine Sci 39(December):589–596. http://nopr.niscair.res.in/handle/123456789/10807
Kim DH, Kim YJ, Hur DS (2014) Artificial neural network based breakwater damage estimation considering tidal level variation. Ocean Eng Elsevier 87:185–190. https://doi.org/10.1016/j.oceaneng.2014.06.001
Raju B, Hegde AV, Chandrashekar O (2015) Computational intelligence on hydrodynamic performance characteristics of emerged perforated quarter circle breakwater. Procedia Eng (Elsevier B.V.) 116(1):118–124. https://doi.org/10.1016/j.proeng.2015.08.272
Sylaios G, Bouchette F, Tsihrintziz VA, Denamiel C (2009) A fuzzy inference system for wind wave modeling. Ocean Eng 36:1358–1365. https://doi.org/10.1016/j.oceaneng.2009.08.016. https://doi.org/10.1016/j.oceaneng.2009.08.016
Patil SG, Mandal S, Hegde AV, Alavandar S (2011) Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater. Ocean Eng (Elsevier) 38(1):186–196. https://doi.org/10.1016/j.oceaneng.2010.10.009
Zanuttigh B, Mizar S, Briganti R (2013) A neural network for the prediction of wave reflection from coastal and harbor structures. Coast Eng (Elsevier B.V.) 80:49–67. http://dx.doi.org/10.1016/j.coastaleng.2013.05.004
Sooraj M (2009) Sliding stability and hydrodynamic performance of emerged semicircular breakwater. M. Tech Thesis, NITK, Surathkal, Mangaluru, India
Sreejith (2015) Sliding stability and hydrodynamic performance of emerged semicircular breakwater. M. Tech Thesis, NITK, Surathkal, Mangaluru, India
Vishal K (2010) Hydrodynamic performance characteristics of one side and two side perforated semicircular breakwater. M. Tech Thesis, NITK, Surathkal, Mangaluru, India
Nishanth N (2008) Sliding stability and hydrodynamic performance of emerged semicircular breakwater M. Tech Thesis, NITK, Surathkal. Mangaluru. India
Azamathulla H, Asce M, Ghani AA (2011) ANFIS-Based approach for predicting the scour depth at culvert outlets 2(February), pp 35–40. https://doi.org/10.1061/(asce)ps.1949-1204.0000066
Karsoliya S (2012) Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture 3:714–717. http://ijettjournal.org/volume-3/issue-6/IJETT-V3I6P206.pdf
Panchal FS, Panchal, M. (2014) Review on methods of selecting number of hidden nodes in artificial neural network 3(11):455–464. http://www.ijcsmc.com/docs/papers/November2014/V3I11201499a19.pdf
Hiremath S, Patra SK (2010) Transmission rate prediction for cognitive radio using adaptive neural fuzzy inference system. In: 2010 international conference on industrial and information systems (ICIIS). http://ieeexplore.ieee.org/document/5578727/
Bataineh KM, Naji M, Saqer M (2011) A Comparison study between various fuzzy clustering algorithms. Jordan J Mech Indust Eng 5(4):335–343. http://jjmie.hu.edu.jo/files/v5n4/JJMIE-230-09.pdf
Hiremath SM, Patra SK, Mishra AK (2012) Extended date rate prediction for cognitive radio using ANFIS with Subtractive Clustering. In: 5th International conference on computers and devices for communication (CODEC), Kolkata, pp 1–4. http://dspace.nitrkl.ac.in/dspace/bitstream/2080/1821/1/Paper_cordic.pdf
Rahmat OK, Hassan A, Alauddin M, Ali M (2005) Generation of fuzzy rules with subtractive clustering. J Teknologi 43(D):143–153. https://doi.org/10.11113/jt.v43.782
Vernieuwe H, Georgieva O, De Baets B, Pauwels VRN, Verhoest NEC, De Troch P (2005) Comparison of data-driven Takagi—Sugeno models of rainfall—discharge dynamics. J Hydrol 302:173–186. https://doi.org/10.1016/j.jhydrol.2004.07.001
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Kundapura, S., Hegde, A.V., Wazerkar, A.V. (2019). Beyond the Data Range Approach to Soft Compute the Reflection Coefficient for Emerged Perforated Semicircular Breakwater. In: Murali, K., Sriram, V., Samad, A., Saha, N. (eds) Proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018). Lecture Notes in Civil Engineering , vol 23. Springer, Singapore. https://doi.org/10.1007/978-981-13-3134-3_21
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