Investigation of undertow in reflective beaches using a GMDH-type neural network

Naeem ABEDIMAHZOON; Hossein MOLAABASI; MirAhmad LASHTEHNESHAEI

Makale detayları

Investigation of undertow in reflective beaches using a GMDH-type neural network

Yazarlar:

Naeem ABEDIMAHZOON | Hossein MOLAABASI | MirAhmad LASHTEHNESHAEI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Engineering and Environmental Sciences 2010 Vol.34 Issue 3, pp.201-213

Belge Tipi:

Makale

Yayın Tarihi:

2/2010

Belge Dili:

Türkçe

Document ID (Org No.):

2000744713

Sıklık:

Üç Yıllık

eISSN:

1303-6157

pISSN:

1300-0160

Özet:

 

Undertow is considered to be one of the dominant mechanisms  in the erosion of beaches.  In this paper, the distribution of  undertow velocity is represented based on experimental evidence.  A  new polynomial model is suggested to calculate undertow velocity,  based on experimental data from the Coastal Laboratory of Kagoshima  University in Japan involving regular waves approaching natural and  reflective beaches.  This study addressed the question of whether  GMDH-type neural networks could be used to estimate the undertow  velocity based on specified variables.  Results indicate that  GMDH-type neural networks, in validation with the data obtained from  the irregular wave experiments performed at the Hydraulic Laboratory  of Imperial College (London, UK), provide an effective means of  efficiently (R2 = 78%) recognizing the patterns in data and  accurately predicting a performance index based on investigated  inputs.

 

Undertow is considered to be one of the dominant mechanisms  in the erosion of beaches.  In this paper, the distribution of  undertow velocity is represented based on experimental evidence.  A  new polynomial model is suggested to calculate undertow velocity,  based on experimental data from the Coastal Laboratory of Kagoshima  University in Japan involving regular waves approaching natural and  reflective beaches.  This study addressed the question of whether  GMDH-type neural networks could be used to estimate the undertow  velocity based on specified variables.  Results indicate that  GMDH-type neural networks, in validation with the data obtained from  the irregular wave experiments performed at the Hydraulic Laboratory  of Imperial College (London, UK), provide an effective means of  efficiently (R2 = 78%) recognizing the patterns in data and  accurately predicting a performance index based on investigated  inputs.

Makale detayları

Investigation of undertow in reflective beaches using a GMDH-type neural network

Yazarlar: Naeem ABEDIMAHZOON | Hossein MOLAABASI | MirAhmad LASHTEHNESHAEI

Yayıncı: TUBITAK

Dergi: Turkish Journal of Engineering and Environmental Sciences 2010 Vol.34 Issue 3, pp.201-213

Belge Tipi: Makale

Yayın Tarihi: 2/2010

Belge Dili: Türkçe

Document ID (Org No.): 2000744713

Sıklık: Üç Yıllık

eISSN: 1303-6157

pISSN: 1300-0160

Sayı

Yazarlar:

Naeem ABEDIMAHZOON | Hossein MOLAABASI | MirAhmad LASHTEHNESHAEI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Engineering and Environmental Sciences 2010 Vol.34 Issue 3, pp.201-213

Belge Tipi:

Makale

Yayın Tarihi:

2/2010

Belge Dili:

Türkçe

Document ID (Org No.):

2000744713

Sıklık:

Üç Yıllık

eISSN:

1303-6157

pISSN:

1300-0160