Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/18538
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Separability indexes and accuracy of neuro-fuzzy classification in Geographic Information Systems for assessment of coastal environmental vulnerability
Authors: Moreno, Navas Juan
Telfer, Trevor
Ross, Lindsay
Contact Email: l.g.ross@stir.ac.uk
Keywords: Neuro-fuzzy classification
Geographic Information System
Separability indexes
Coastal environmental vulnerability
Issue Date: Nov-2012
Publisher: Elsevier
Citation: Moreno Navas J, Telfer T & Ross L (2012) Separability indexes and accuracy of neuro-fuzzy classification in Geographic Information Systems for assessment of coastal environmental vulnerability, Ecological Informatics, 12, pp. 43-49.
Abstract: The aim of this study was the development, evaluation and analysis of a neuro-fuzzy classifier for a supervised and hard classification of coastal environmental vulnerability due to marine aquaculture using minimal training sets within a Geographic Information System (GIS). The neuro-fuzzy classification model NEFCLASS‐J, was used to develop learning algorithms to create the structure (rule base) and the parameters (fuzzy sets) of a fuzzy classifier from a set of labeled data. The training sites were manually classified based on four categories of coastal environmental vulnerability through meetings and interviews with experts having field experience and specific knowledge of the environmental problems investigated. The inter-class separability estimations were performed on the training data set to assess the difficulty of the class separation problem under investigation. The two training data sets did not follow the assumptions of multivariate normality. For this reason Bhattacharyy and Jeffries-Matusita distances were used to estimate the probability of correct classification. Further evaluation and analysis of the quality of the classification achieved low values of quantity and allocation disagreement and a good overall accuracy. For each of the four classes the user and producer values for accuracy were between 77% and 100%. In conclusion, the use of a neuro-fuzzy classifier for a supervised and hard classification of coastal environmental vulnerability demonstrated an ability to derive an accurate and reliable classification using a minimal number of training sets.
Type: Journal Article
URI: http://hdl.handle.net/1893/18538
DOI Link: http://dx.doi.org/10.1016/j.ecoinf.2012.06.006
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Heriot-Watt University
Aquaculture
Aquaculture

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