کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
496218 862852 2013 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluation of reservoir sedimentation using data driven techniques
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Evaluation of reservoir sedimentation using data driven techniques
چکیده انگلیسی


• There are large constraints in conventional direct methods of sediment deposition estimation.
• In the present study, different data driven techniques were tried to find suitable model for sediment deposition estimation.
• It was found that soft computing techniques can play important role in such modelling.
• The evolutionary genetic programming technique found to the best modelling technique.

The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 13, Issue 8, August 2013, Pages 3567–3581
نویسندگان
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