A neural-fuzzy approach to classify the ecological status in surface waters.
A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive.
Ocampo-Duque
William
W
School of Chemical and Process Engineering, Rovira i Virgili University, Av. Países Catalanes 26, 43007 Tarragona, Spain. waocampo@urv.cat
Schuhmacher
Marta
M
Domingo
José L
JL
D000465:Algorithms; D015984:Causality; D017753:Ecosystem; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D045483:Rivers / Q000737:chemistry; D035501:Uncertainty; D014876:Water Pollution