Separability indexes and accuracy of neuro-fuzzy classification in Geographic Information Systems for assessment of coastal environmental vulnerability

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o 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. Marine environmental vulnerability is the susceptibility of marine and transitional water resources to various anthropogenic activities and released contaminants. Many types of ecological and environmental data are qualitative or use discrete categories and boundaries and for these reasons are difficult to incorporate into classification schemes designed to produce a numerical index of ecological quality (Silvert, 1997). Consequently, there is a clear need for innovative models which can develop limited qualitative or quantitative data into objective decision support tools for coastal management. Geographic Information Systems (GIS) provide an easy-to-use graph-ical user interface for visualization and interrogation of results, and as an input for data for the development of spatial environmental models. The …