Exploring the ecological status of human altered streams through Generative Topographic Mapping

Authors:
A. Vellido;E. Martí;J. Comas;I. Rodríguez-Roda;F. Sabater
Affiliations:
Department of Computing Languages and Systems (LSI), Polytechnic University of Catalonia (UPC), C. Jordi Girona, 1-3, 08034 Barcelona, Spain;Centre d'Estudis Avançats de Blanes-CSIC, Accés a la Cala de St. Francesc, 14, 17300 Blanes, Girona, Spain;Chemical and Environmental Engineering Laboratory (LEQUIA), University of Girona, Campus Montilivi s/n, 17071 Girona, Spain;Chemical and Environmental Engineering Laboratory (LEQUIA), University of Girona, Campus Montilivi s/n, 17071 Girona, Spain;Department of Ecology, University of Barcelona (UB), C. Diagonal, 645, 08028 Barcelona, Spain
Venue:
Environmental Modelling & Software
Year:
2007

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Abstract

The STREAMES (STream REAch Management, an Expert System) European project is an international enterprise for the development of a knowledge-based environmental decision support system to assist water managers with their decision making tasks. It involves the evaluation of the effect of substantial nutrient loads on the overall water quality and ecological status of stream ecosystems. Empirical data for the knowledge base come from several streams located throughout Europe and Israel, with emphasis on streams from the Mediterranean region. These data comprise several types of variables, including physical, chemical and biological parameters. The complexity of the data limits the amount and completeness of the available information. This study explores how similar the selected streams are on the basis of the ecological descriptors measured. The analysis of these similarities helps us to ascertain whether the same model of ecological status might hold over the variety of streams under consideration. The available data are explored and analysed through reconstruction, visualization and clustering using the Generative Topographic Mapping (GTM), a neural network-based model. Amongst the many advantages of the probabilistic setting of GTM, one is especially relevant to the problem at hand: its ability to handle and reconstruct missing data in a principled way.