Implementation of neural networks for classification of moss and lichen samples on the basis of gamma-ray spectrometric analysis.
Environ Monit Assess
; 130(1-3): 245-53, 2007 Jul.
Article
in En
| MEDLINE
| ID: mdl-17057958
ABSTRACT
Mosses and lichens have an important role in biomonitoring. The objective of this study is to develop a neural network model to classify these plants according to geographical origin. A three-layer feed-forward neural network was used. The activities of radionuclides ((226)Ra, (238)U, (235)U, (40)K, (232)Th, (134)Cs, (137)Cs and (7)Be) detected in plant samples by gamma-ray spectrometry were used as inputs for neural network. Five different training algorithms with different number of samples in training sets were tested and compared, in order to find the one with the minimum root mean square error. The best predictive power for the classification of plants from 12 regions was achieved using a network with 5 hidden layer nodes and 3,000 training epochs, using the online back-propagation randomized training algorithm. Implementation of this model to experimental data resulted in satisfactory classification of moss and lichen samples in terms of their geographical origin. The average classification rate obtained in this study was (90.7 +/- 4.8)%.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Spectrometry, Gamma
/
Neural Networks, Computer
/
Bryophyta
/
Lichens
Type of study:
Clinical_trials
/
Prognostic_studies
Language:
En
Journal:
Environ Monit Assess
Journal subject:
SAUDE AMBIENTAL
Year:
2007
Document type:
Article
Publication country:
HOLANDA
/
HOLLAND
/
NETHERLANDS
/
NL
/
PAISES BAJOS
/
THE NETHERLANDS