Abstract
Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and also to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwaters (EPQCB) are artificial concrete breakwaters consisting of a curved perforated face fronting the waves with a vertical wall on rear side and a base slab resting on a low rubble mound base. The perforated curved front face has advantages like energy dissipation and good stability with less material as it is hollow inside. The estimation of hydrodynamic performance characteristics of EPQCB by physical model studies is complex, expensive and time consuming. Hence, computational intelligence (CI) methods are adopted for the evaluation of the performance characteristics like reflection, dissipation, transmission, run-up, rundown etc. A number of CI methods like Artificial Neural Network (ANN), Fuzzy logic, and hybrids such as ANFIS, ANN-PCO (particle swarm optimization), ANN-ACO etc., are available and are being used. The paper presents the work carried out to predict the dependent output variable of loss coefficient (Kl) beyond the range of values of one of the input variables i.e., wave period (T) adopted in present work, using the input data on variables of wave height (H), wave period (T), structure height (hs), water depth (d), radius of the breakwater (R), spacing of perforations (S) and diameter of perforations (D) using ANFIS. For this purpose, both the conventional method of data segregation and also a new method called ‘beyond data range’ method are used for both training the ANFIS models and also to predict the dependent variable. Further, the input data was fed to the models in both dimensional and non-dimensional form in order to understand the effect of using non-dimensional data in place of dimensional parametric data. The performance of ANFIS models for all the four cases mentioned above was studied and it was found that prediction using conventional method with non-dimensional parameters performed better than other three methods. ANFIS models can be used to predict the performance characteristic Kl of EPQCB beyond the input data range of wave period T.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Areerachakul, S.: Comparison of ANFIS and ANN for estimation of biochemical oxygen demand parameter in surface water. World Academy of Science, Engineering and Technology, vol. 6, April 26, 2012
Balakrishna, K.: Hydrodynamic performance characteristics of emerged perforated quarter circle breakwater. M. Tech Thesis, NITK, Surathkal, India (2014)
Dhinakaran, G., Sundar, V., Graw, K.: Effect of perforations and rubble mound height on wave transformation characteristics of surface piercing semicircular breakwaters. J. Ocean Eng. 36(15), 1182–1198 (2009)
ErolKeskin, M., Taylan, D., Terzi, O.: Adaptive neural-based fuzzy inference system (ANFIS) approach for modeling hydrological time series. Hydrological Sciences Journal 51(4), 588–598 (2006)
Londhe, S.N.: Soft computing approach for real-time estimation of missing wave heights. Ocean Engineering 35, 1080–1089 (2008)
Patil, S.G.: Computational Intelligence in prediction of wave transmission for horizontally interlaced multilayer moored floating pipe breakwater. Ph.D thesis, NITK, Surathkal, India (2012)
Qie, L., Zhang, X., Jiang, X., Qin, Y.: Research on partial coefficients for design of quarter circle caisson breakwater. J. Marine Science Applications 12, 65–71 (2013)
Rushil, G., Kiriti, S., Hegde, A.V.: Quarter circular breakwater: prediction of transmission using multiple regression and artificial neural network. Marine Technology Society Journal 48, 92–98 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hegde, A.V., Raju, B. (2015). Conventional Prediction vs Beyond Data Range Prediction of Loss Coefficient for Quarter Circle Breakwater Using ANFIS. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-19222-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19221-5
Online ISBN: 978-3-319-19222-2
eBook Packages: Computer ScienceComputer Science (R0)