An Application of Fuzzy C-Means, Fuzzy Cognitive Maps, and Fuzzy Rules to Forecasting First Arrival Date of Avian Spring Migrants

In this study, we propose an approach based on the advanced fuzzy techniques such as Fuzzy C-Means and Fuzzy Cognitive Maps to cluster the birds species, based on the information of first arrival date, into more coherent and uniform groups. The birds are very suitable subject for modelling the climate changes. Very popular indicator to forecast bird migration dynamic is the first arrival date. In many reported studies, this indicator is shown as very useful. However, there is still a lack of precise methods grouping the birds into the classes in satisfying manner producing detailed information about species and the relations between them. As evidenced in the experimental series section, the proposed approach enables the researchers and practitioners working with that important area of ecology to observe subtle dependencies between various bird species. Moreover, this work sheds the light on the novel application of both Fuzzy C-Means and Fuzzy Cognitive Maps as the efficient tools to analyse the ecological data collected in changing climatic environment.