To assign the importance of each input variable on the scour depth, the MT model was selected to perform a sensitivity analysis. The analysis was conducted such that, one parameter of Equation (4) was eliminated each time to evaluate the effect of that input on output. Results of the analysis demonstrated that D/d_{50} (R = 0.48, RMSE = 5.52, RSE = 22.86, MAE = 1.94, and RAE = 26.42) is the most effective parameter on the maximum scour depth whereas d_{d}/b (R = 0.95, RMSE = 0.21, RSE = 0.11, MAE = 0.16, and RAE = 0.07) has the least influence on the K_{d(T*)} for the MT model, respectively. The other effective parameters on the K_{d(T*)} parameter include h/D, T*, , D/b, and U/U_{c} which were ranked from higher to lower values, respectively. The statistical error parameters yielded from the sensitivity analysis are given in Table 8. Also, the results of sensitivity analysis indicated that D/d_{50} is the most important parameter in modeling of the maximum scour depth by the MT network. This study has proved that the MT model as an adaptive learning network can be used as a powerful soft computing tool for predicting the prediction of maximum scour depth around piers with debris accumulation as well as the other AI methods.

Table 8

Input parameters . | R . | RMSE . | RSE . | MAE . | RAE . |
---|---|---|---|---|---|

0.89 | 0.36 | 0.27 | 0.25 | 0.18 | |

0.78 | 0.52 | 19.23 | 0.44 | 2.32 | |

0.95 | 0.21 | 0.11 | 0.16 | 0.07 | |

0.94 | 0.26 | 0.43 | 0.21 | 0.17 | |

0.93 | 0.25 | 0.24 | 0.20 | 0.12 | |

0.71 | 0.52 | 0.92 | 0.35 | 0.5 | |

0.48 | 5.52 | 22.86 | 1.94 | 26.42 |

Input parameters . | R . | RMSE . | RSE . | MAE . | RAE . |
---|---|---|---|---|---|

0.89 | 0.36 | 0.27 | 0.25 | 0.18 | |

0.78 | 0.52 | 19.23 | 0.44 | 2.32 | |

0.95 | 0.21 | 0.11 | 0.16 | 0.07 | |

0.94 | 0.26 | 0.43 | 0.21 | 0.17 | |

0.93 | 0.25 | 0.24 | 0.20 | 0.12 | |

0.71 | 0.52 | 0.92 | 0.35 | 0.5 | |

0.48 | 5.52 | 22.86 | 1.94 | 26.42 |

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