Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/12974
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dc.contributor.authorKuntoji, G.S.
dc.contributor.authorRao, S.
dc.contributor.authorManu
dc.contributor.authorMandal, S.
dc.date.accessioned2020-03-31T08:42:34Z-
dc.date.available2020-03-31T08:42:34Z-
dc.date.issued2017
dc.identifier.citationInternational Journal of Ecology and Development, 2017, Vol.32, 2, pp.141-155en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/12974-
dc.description.abstractTandem breakwater plays a unique role in protecting the ports. It is an innovative breakwater concept consisting of conventional breakwater and a submerged reef operating in tandem. As the depth-limiting behaviour of reef, the tandem possesses less design risk for extreme events. For a tandem breakwater, the transmitted wave over the submerged reef plays avital role in the safety of the emergent breakwater. Coastal structures like breakwaters are massive in terms of size as well as in the costs. Any structure before finally being constructed has to be subjected to model investigations for its safety against the design parameters. The soft computing techniques such as ANFIS (Adaptive Neuro Fuzzy Inference system) and SVM (Support Vector Machine)models are developed using experimental data points to predict the hydraulic performance of submerged reef of tandem breakwater. The performances of two models are validated with measured data, with the help of statistical measures namelyRMSE (Root MeanSquare-Error), CC (CorrelationCo-efficient), SI (Scatter-Index) andNSE (Nash-Sutcliff Efficiency). The results testify that SVM model performed better with 0.965 CC, 0.0557 RMSE, 0.9113 NSE and 0.1503 SI compared to ANFIS model with 0.935 CC, 0.0754 RMSE, 0.869 NSE and 0.00233 SI. 2017 by International Journal of Ecology & Development.en_US
dc.titlePerformance evaluation of ANFIS and SVM model in prediction of wave transmission over submerged reef of tandem breakwateren_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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