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Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling

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Abstract

In the present study, the Group method of data handling (GMDH) network was utilized to predict the scour depth below pipelines. GMDH network was developed using back propagation. Input parameters that were considered as effective parameters on the scour depth included those of sediment size, geometry of pipeline, and approaching flow characteristics. Training and testing performances of the GMDH networks have been carried out using nondimensional data sets that were collected from the literature. These data sets are related to the two main situations of pipelines scour experiments namely clear-water and live-bed conditions. The testing results of performances were compared with the support vector machines (SVM) and existing empirical equations. The GMDH network indicated that using of back propagation produced lower error of scour depth prediction than those obtained using the SVM and empirical equations. Also, the effects of many input parameters on the scour depth have been investigated.

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Authors and Affiliations

  1. Department of Civil Engineering, Shahid Bahonar University, P.O. Box 76169133, Kerman, Iran

    Mohammad Najafzadeh & Gholam-Abbas Barani

  2. River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Penang, Malaysia

    Hazi Mohammad Azamathulla

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  1. Mohammad Najafzadeh
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  2. Gholam-Abbas Barani
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  3. Hazi Mohammad Azamathulla
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Correspondence to Mohammad Najafzadeh.

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Najafzadeh, M., Barani, GA. & Azamathulla, H.M. Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling. Neural Comput & Applic 24, 629–635 (2014). https://doi.org/10.1007/s00521-012-1258-x

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