Matches in SemOpenAlex for { <https://semopenalex.org/work/W2063983072> ?p ?o ?g. }
- W2063983072 endingPage "150" @default.
- W2063983072 startingPage "143" @default.
- W2063983072 abstract "Estimation of wave parameters is of great importance in coastal activities such as design studies for harbour, inshore and offshore structures, coastal erosion, sediment transport and wave energy estimation. Traditional methods like semi empirical formulations and numerical models have disadvantages of excessive data requirement, time consumption and are tedious to carry out.Artificial Intelligent methods like ANN (Artificial Neural Network) are powerful and flexible modeling tool is being widely used in coastal engineering field since last two decades for solving various problems related to time series forecasting of waves and tides; prediction of sea-bed liquefaction and scour depth; and estimation of design parameters of coastal engineering structures. It imbibes the qualities of exploiting non-linearity, adaptability to adjust free parameters by mapping input output data sets using various learning algorithms and fault tolerance.Using input and output information, intelligent systems enabled to estimate the hidden law behind the information and to find their relations among them. In the present study attempt has been made to predict waves at New Mangalore Port Trust (NMPT) located along the west coast of India using Feed Forward Back Propagation (FFBP) with LM algorithm and a recurrent network called Non-linear Auto Regressive with exogenous input(NARX) network. Field data of NMPT has been used to train and test the network performance, which are measured in terms of mean square error (mse) and correlation coefficient (r). Effect of network architecture on the performance of model has been studied.Correlation coefficient is found to be 0.94 in case of NARX predictions indicating better performance than FFBP network whose ‘r’ value is 0.9. It was found that for time series prediction NARX network outperform FFBP network not only in terms of accuracy but also in terms of time required for computation." @default.
- W2063983072 created "2016-06-24" @default.
- W2063983072 creator A5061154862 @default.
- W2063983072 creator A5071790407 @default.
- W2063983072 date "2015-01-01" @default.
- W2063983072 modified "2023-09-29" @default.
- W2063983072 title "Wave Prediction Using Neural Networks at New Mangalore Port along West Coast of India" @default.
- W2063983072 cites W1964815225 @default.
- W2063983072 cites W1975016606 @default.
- W2063983072 cites W1978137205 @default.
- W2063983072 cites W1982723853 @default.
- W2063983072 cites W1994205836 @default.
- W2063983072 cites W2001157011 @default.
- W2063983072 cites W2008578468 @default.
- W2063983072 cites W2022890795 @default.
- W2063983072 cites W2026046132 @default.
- W2063983072 cites W2043550022 @default.
- W2063983072 cites W2045017715 @default.
- W2063983072 cites W2071407752 @default.
- W2063983072 cites W2071747709 @default.
- W2063983072 cites W2136653283 @default.
- W2063983072 cites W2146235859 @default.
- W2063983072 cites W2146293088 @default.
- W2063983072 cites W2161266294 @default.
- W2063983072 cites W2169046426 @default.
- W2063983072 cites W2180850383 @default.
- W2063983072 doi "https://doi.org/10.1016/j.aqpro.2015.02.020" @default.
- W2063983072 hasPublicationYear "2015" @default.
- W2063983072 type Work @default.
- W2063983072 sameAs 2063983072 @default.
- W2063983072 citedByCount "31" @default.
- W2063983072 countsByYear W20639830722016 @default.
- W2063983072 countsByYear W20639830722017 @default.
- W2063983072 countsByYear W20639830722018 @default.
- W2063983072 countsByYear W20639830722019 @default.
- W2063983072 countsByYear W20639830722020 @default.
- W2063983072 countsByYear W20639830722021 @default.
- W2063983072 countsByYear W20639830722022 @default.
- W2063983072 countsByYear W20639830722023 @default.
- W2063983072 crossrefType "journal-article" @default.
- W2063983072 hasAuthorship W2063983072A5061154862 @default.
- W2063983072 hasAuthorship W2063983072A5071790407 @default.
- W2063983072 hasBestOaLocation W20639830721 @default.
- W2063983072 hasConcept C105795698 @default.
- W2063983072 hasConcept C111368507 @default.
- W2063983072 hasConcept C119857082 @default.
- W2063983072 hasConcept C124101348 @default.
- W2063983072 hasConcept C127313418 @default.
- W2063983072 hasConcept C127413603 @default.
- W2063983072 hasConcept C139945424 @default.
- W2063983072 hasConcept C154945302 @default.
- W2063983072 hasConcept C162284963 @default.
- W2063983072 hasConcept C177606310 @default.
- W2063983072 hasConcept C187320778 @default.
- W2063983072 hasConcept C18903297 @default.
- W2063983072 hasConcept C199104240 @default.
- W2063983072 hasConcept C202444582 @default.
- W2063983072 hasConcept C2780092901 @default.
- W2063983072 hasConcept C33923547 @default.
- W2063983072 hasConcept C41008148 @default.
- W2063983072 hasConcept C42536954 @default.
- W2063983072 hasConcept C50644808 @default.
- W2063983072 hasConcept C70620910 @default.
- W2063983072 hasConcept C86803240 @default.
- W2063983072 hasConcept C9652623 @default.
- W2063983072 hasConceptScore W2063983072C105795698 @default.
- W2063983072 hasConceptScore W2063983072C111368507 @default.
- W2063983072 hasConceptScore W2063983072C119857082 @default.
- W2063983072 hasConceptScore W2063983072C124101348 @default.
- W2063983072 hasConceptScore W2063983072C127313418 @default.
- W2063983072 hasConceptScore W2063983072C127413603 @default.
- W2063983072 hasConceptScore W2063983072C139945424 @default.
- W2063983072 hasConceptScore W2063983072C154945302 @default.
- W2063983072 hasConceptScore W2063983072C162284963 @default.
- W2063983072 hasConceptScore W2063983072C177606310 @default.
- W2063983072 hasConceptScore W2063983072C187320778 @default.
- W2063983072 hasConceptScore W2063983072C18903297 @default.
- W2063983072 hasConceptScore W2063983072C199104240 @default.
- W2063983072 hasConceptScore W2063983072C202444582 @default.
- W2063983072 hasConceptScore W2063983072C2780092901 @default.
- W2063983072 hasConceptScore W2063983072C33923547 @default.
- W2063983072 hasConceptScore W2063983072C41008148 @default.
- W2063983072 hasConceptScore W2063983072C42536954 @default.
- W2063983072 hasConceptScore W2063983072C50644808 @default.
- W2063983072 hasConceptScore W2063983072C70620910 @default.
- W2063983072 hasConceptScore W2063983072C86803240 @default.
- W2063983072 hasConceptScore W2063983072C9652623 @default.
- W2063983072 hasLocation W20639830721 @default.
- W2063983072 hasOpenAccess W2063983072 @default.
- W2063983072 hasPrimaryLocation W20639830721 @default.
- W2063983072 hasRelatedWork W2025655116 @default.
- W2063983072 hasRelatedWork W2188032833 @default.
- W2063983072 hasRelatedWork W2353203422 @default.
- W2063983072 hasRelatedWork W2363345704 @default.
- W2063983072 hasRelatedWork W2380811019 @default.
- W2063983072 hasRelatedWork W2389564146 @default.
- W2063983072 hasRelatedWork W3014837209 @default.
- W2063983072 hasRelatedWork W4253215193 @default.