دانلود مقاله ISI انگلیسی شماره 79495
عنوان فارسی مقاله

مدل رگرسیون برای مشکلات انتقال رسوب با استفاده از برنامه نویسی ژنتیک نمادین چند ژن

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79495 2014 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Regression model for sediment transport problems using multi-gene symbolic genetic programming
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers and Electronics in Agriculture, Volume 103, April 2014, Pages 82–90

کلمات کلیدی
برنامه نویسی ژنتیک؛ حرکت اولیه؛ انتقال رسوب؛ مجموع بار بستر؛ جریان با پوشش گیاهی
پیش نمایش مقاله
پیش نمایش مقاله مدل رگرسیون برای مشکلات انتقال رسوب با استفاده از برنامه نویسی ژنتیک نمادین چند ژن

چکیده انگلیسی

Sediment transport modeling problems are complex due to the multi-dimensionality of the problems, along with their nonlinear interdependence. Also, in river hydraulics, phenomena are stochastic and variables are measured with uncertainties which are unavoidable. Dimensional and regression analyses have been employed in the past but have associated limitations. As a robust modeling tool, genetic programming was used to develop predictor models for three different but related problems of sediment transport-vegetated flow, incipient motion and total bed load prediction. A relatively new development over the conventional genetic programming-multi-gene symbolic regression was used to model functional relationships that were able to generalize highly nonlinear variations in data as well as predict system behavior from independent input data in all the three cases. The algorithmic parameters for genetic programming technique were resolved iteratively, varying based on problems in context. For all the three models developed, model efficiency criteria were found out and presented and the performance of the present model was compared with several past models for the same data points. The models developed herein were able to generalize the underlying relationships in the presented data as well as were able to predict values for unknown data with high accuracy.

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