Diatoms Classification with Weighted Averaging Fuzzy Operators for Eutrophication Prevention

The level of nutrients determines the triggering point of the eutrophication process, so it is very important to monitor these levels. Diatoms react rapidly on nutrient changes and that makes them ideal eutrophication bio-indicators. In the relevant literature there is known ecological reference for some diatoms, but for many of them these indicator features remain unidentified. In order to fill this gap and deal with disadvantages of the previous used methods, this research chapter aims to a novel fuzzy algorithm for classification of diatoms for eutrophication prevention. By using this algorithm, each diatom will be classified and based on results from the diatom models will be recommended for such objective. The proposed method uses sigmoid distribution to reveal the diatom-indictor relationship. Combined with weighted averaging fuzzy operators the experimental results have verified and discovered several diatom indicators that can be used for prevention. Once the diatom is found in the water sample, the expert looks up in the database and identifies the health state of the ecosystem. This also can be done for metal parameters, not just for nutrients.

[1]  Tao Wang,et al.  A survey of fuzzy decision tree classifier , 2009 .

[2]  M. Zaman-Allah,et al.  Ecological engineering : from concepts to applications Nodular diagnosis for ecological engineering of the symbiotic nitrogen fixation with legumes , 2011 .

[3]  Tamás D. Gedeon,et al.  Pattern Trees Induction: A New Machine Learning Method , 2008, IEEE Transactions on Fuzzy Systems.

[4]  Louis Wehenkel,et al.  A complete fuzzy decision tree technique , 2003, Fuzzy Sets Syst..

[5]  Xizhao Wang,et al.  On the optimization of fuzzy decision trees , 2000, Fuzzy Sets Syst..

[6]  Andreja Naumoski,et al.  Classifying diatoms into trophic state index classes with novel classification algorithm , 2010 .

[7]  J. Sinkeldam,et al.  A coded checklist and ecological indicator values of freshwater diatoms from The Netherlands , 1994, Netherland Journal of Aquatic Ecology.

[8]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[9]  John P. Smol,et al.  The diatoms: applications for the environmental and earth sciences , 2012 .

[10]  Vicenç Torra,et al.  Modeling decisions - information fusion and aggregation operators , 2007 .

[11]  Cezary Z. Janikow,et al.  Fuzzy decision trees: issues and methods , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Andreja Naumoski,et al.  Diatom Classification with Novel Bell Based Classification Algorithm , 2010, ICT Innovations.

[13]  J. Ross Quinlan,et al.  Decision trees and decision-making , 1990, IEEE Trans. Syst. Man Cybern..

[14]  Saso Dzeroski,et al.  Predicting chemical parameters of the water from diatom abudance in lake Prespa and its tributaries , 2009, ITEE.

[15]  T. Allan Smith,et al.  Cyril and Methodius , 2012 .

[16]  Michel Coste,et al.  Field transfer of periphytic diatom communities to assess short-term structural effects of metals (Cd, Zn) in rivers. , 2002, Water research.

[17]  Alberto Suárez,et al.  Globally Optimal Fuzzy Decision Trees for Classification and Regression , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Sašo Džeroski,et al.  Learning habitat models for the diatom community in Lake Prespa , 2010 .

[19]  M. Shaw,et al.  Induction of fuzzy decision trees , 1995 .