article 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 sitesweremanuallyclassified based on four categoriesof coastal environmental vulnerability through meetings and interviews with experts having field experience and specific knowledgeoftheenvironmentalproblemsinvestigated.Theinter-classseparabilityestimationswereperformed onthetraining datasettoassess the difficulty of theclass separation problemunderinvestigation. The twotrain- ing data sets did not follow the assumptions of multivariate normality. For thisreason Bhattacharyy and Jeffries- Matusita distanceswere used to estimate the probability of correct classification. Further evaluation and analysis ofthequalityofthe classification achievedlow valuesofquantity and allocation disagreementand agoodoverall 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 environ- mental vulnerability demonstrated an ability to derive an accurate and reliable classification using a minimal number of training sets.
['Juan Moreno Navas', 'Trevor Telfer', 'Lindsay G. Ross']
Separability indexes and accuracy of neuro-fuzzy classification in Geographic Information Systems for assessment of coastal environmental vulnerability