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Article citations


Witten, I.H. and Frank, E. (2005) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco.

has been cited by the following article:

  • TITLE: M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

    AUTHORS: R. Biabani, M. Meftah Halaghi, Kh. Ghorbani

    KEYWORDS: Abutments, Scour Depth, M5 Model Tree, Genetic Programming Model (GP)

    JOURNAL NAME: Open Journal of Geology, Vol.6 No.9, September 9, 2016

    ABSTRACT: Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R2 = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.