کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1705352 1012430 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Performance evaluation of artificial neural network approaches in forecasting reservoir inflow
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مکانیک محاسباتی
پیش نمایش صفحه اول مقاله
Performance evaluation of artificial neural network approaches in forecasting reservoir inflow
چکیده انگلیسی

This study investigates the potential of Time Lag Recurrent Neural Networks (TLRN) for modeling the daily inflow into Eleviyan reservoir, Iran. TLRN are extended with short term memory structures that have local recurrent connections, thus making them an appropriate model for processing temporal (time-varying) information. For this study, the daily inflow into Eleviyan reservoir between years 2004–2007 was considered. To compare the performance of TLRN, a back propagation neural network was used. The TLRN model with gamma memory structure, eight input layer nodes, two hidden layer and one output layer (8-2-1) was found performing best out of three different models used in forecasting daily inflow. A comparison of results with back propagation neural network suggest that neither TLRN nor back propagation approaches were good in forecasting high inflow but, both approaches perform well when used to forecast low inflow values. However, statistical test suggests that both TLRN and back propagation neural network models were able to reproduce similar basic statistics as that of the actual data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematical Modelling - Volume 36, Issue 6, June 2012, Pages 2649–2657
نویسندگان
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