| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4375026 | Ecological Informatics | 2012 | 7 Pages |
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.
► A neuro-fuzzy system was developed to classify coastal environmental vulnerability. ► A minimal number of training sets were used. ► The inter-class separability estimations were performed to assess the classification. ► The classification achieved low allocation and quantity disagreement values.
