The prediction accuracy of the fuzzy diatom models depends on both the manner of defining the fuzzy sets used (their number, shape and the parameters of the membership function (MF)) and the kind of the similarity metric used. In this paper, we define new similarity metric, which takes into the account the maximum number of diatoms’ abundance in specific environmental parameter range. The inverse sigmoid MF is used to shape each MF to describe this relationship, in order to produce more accurate models. This improvement of the ecological modelling is achieved through the process of evaluation results for interpretability; higher prediction accuracy and over fitting resistant. The evaluation results compared with classical classification algorithms have confirmed these findings. Based on these results, one model for each water-quality category class is presented and discussed. From ecological point of view, each model is verified with the existing diatom indicator references found in literature by the biological expert.
Inverse sigmoid distribution fuzzy membership function similarity metric water quality models