کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
708135 1461096 2015 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions
ترجمه فارسی عنوان
استفاده از شبکه های عصبی مصنوعی و مدل های برنامه نویسی ژنتیکی برای برآورد میدان های سرعت طولی در اتصالات کانال باز
کلمات کلیدی
اتصال کانال باز شبکه های عصبی مصنوعی، برنامه نویسی ژنتیک، زمینه های سرعت طولی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
چکیده انگلیسی


• A genetic programming model is provided and compared with the multi-layer perceptron artificial neural network method.
• A closed form equation for the prediction of the velocity field in open channel junction is derived.
• Both methods estimate very well the velocity fields for different flow ratios in an open channel junction.

Estimating the accurate longitudinal velocity fields in an open channel junction has a great impact on hydraulic structures such as irrigation and drainage channels, river systems and sewer networks. In this study, Genetic Programming (GP) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) were modeled and compared to find an analytical formulation that could present a continuous spatial description of velocity in open channel junction by using discrete information of laboratory measurements. Three direction coordinates of each point of the fluid flow and discharge ratio of main to tributary channel were used as inputs to the GP and ANN models. The training and testing of the models were performed according to the published experimental data from the related literature. To find the accurate prediction ability of GP and ANN models in cases with minor training dataset, the models were compared with various percents of allocated data to train dataset. New formulations were obtained from GP and ANN models that can be applied for practical longitudinal velocity field prediction in an open channel junction. The results showed that ANN model by Root Mean Squared Error (RMSE) of 0.068 performs better than GP model by RMSE of 0.162, and that ANN can model the longitudinal velocity field with small population of train dataset with high accuracy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Flow Measurement and Instrumentation - Volume 41, March 2015, Pages 81–89
نویسندگان
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