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Predicting performance of vegetative...

Andriyas, Sanyogita.

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Predicting performance of vegetative filter strips using artificial neural networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting performance of vegetative filter strips using artificial neural networks./
Author:	Andriyas, Sanyogita.
Description:	202 p.
Notes:	Source: Masters Abstracts International, Volume: 42-06, page: 2257.
Contained By:	Masters Abstracts International42-06.
Subject:	Engineering, Agricultural. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ90627
ISBN:	0612906272

Predicting performance of vegetative filter strips using artificial neural networks.
Andriyas, Sanyogita.

Predicting performance of vegetative filter strips using artificial neural networks. - 202 p.

Source: Masters Abstracts International, Volume: 42-06, page: 2257.

Thesis (M.Sc.)--University of Guelph (Canada), 2004.

The vegetative filter strips (VFS) are a best management practice. For quantifying the movement and amount of sediments and nutrients, the performance of VFS has to be modelled. Here artificial neural networks (ANN'S) were investigated, to model VFS and then compared to GRAPH. Data available from the literature and recent experiments were used.

ISBN: 0612906272Subjects--Topical Terms:

1019504
Engineering, Agricultural.

Predicting performance of vegetative filter strips using artificial neural networks.
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035 $a (UnM)AAIMQ90627
035 $a AAIMQ90627
040 $a UnM $c UnM
100 1 $a Andriyas, Sanyogita. $3 1901621
245 1 0 $a Predicting performance of vegetative filter strips using artificial neural networks.
300 $a 202 p.
500 $a Source: Masters Abstracts International, Volume: 42-06, page: 2257.
500 $a Adviser: Satish C. Negi.
502 $a Thesis (M.Sc.)--University of Guelph (Canada), 2004.
520 $a The vegetative filter strips (VFS) are a best management practice. For quantifying the movement and amount of sediments and nutrients, the performance of VFS has to be modelled. Here artificial neural networks (ANN'S) were investigated, to model VFS and then compared to GRAPH. Data available from the literature and recent experiments were used.
520 $a Artificial runoff was created. Flow samples were analysed for concentrations for total suspended solids, total phosphorus and soluble phosphorus, and particle size distribution. Available input-output data sets were used to train and test a multi-layered perceptron using back propagation (BP) algorithm and a radial basis function neural network using fuzzy c-means clustering algorithm.
520 $a Sensitivity tests were done for finding optimum architectures of neural networks. The statistical analysis and comparisons between predicted and observed values for the three models showed that a BP network with 15 hidden units can model the performance of VFS efficiently, including the trapping of soluble P. They could predict the outputs, even without the particle size distribution. ANN'S have to be trained before being used to predict the outputs. GRAPH is mobile and could be successfully used for verification, since it takes into account the physical processes going on.
590 $a School code: 0081.
650 4 $a Engineering, Agricultural. $3 1019504
650 4 $a Artificial Intelligence. $3 769149
690 $a 0539
690 $a 0800
710 2 0 $a University of Guelph (Canada). $3 1018650
773 0 $t Masters Abstracts International $g 42-06.
790 1 0 $a Negi, Satish C., $e advisor
790 $a 0081
791 $a M.Sc.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ90627