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Ecological Applications of Fuzzy Logic

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Ecological Informatics

1.6 Conclusions

Heterogeneous and imprecise ecological data and vague expert knowledge can be integrated more effectively using fuzzy approach. Fuzzy logic provides the means to combine numerical data and linguistic statements and to process both of them in one simulation step. Fuzzy sets with no sharply defined boundaries reflect better the continuous character of nature. The number of applications of fuzzy sets and fuzzy logic in ecological modelling and data analysis is constantly growing.

There also are an increasing number of applications of hybrid systems which combine the fuzzy techniques with other techniques, e.g. probabilistic approach, linear programming, neural networks, cellular automata or GIS technique. An increasing interest in the development of fuzzy expert systems for environmental management and engineering can also be expected.

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References

  • Ameskamp M (1997) Three-dimensional rule-based continuous soil modelling. Ph.D. Thesis, Institut für Informatik und Praktische Mathematik, Christian-Albrechts-Universität, Kiel

    Google Scholar 

  • Bárdossy A, Bogardi I, Kelly WE (1989) Geostatistics utilizing imprecise (fuzzy) information. Fuzzy Sets and Systems, 31/3: 311–327

    Article  Google Scholar 

  • Bárdossy A, Bogardi I, Kelly WE (1990) Kriging with imprecise (fuzzy) variograms, I: theory. Math. Geology, 22/3: 63–79

    Article  Google Scholar 

  • Bárdossy A, Duckstein L (1995) Fuzzy rule-based modeling with applications to geophysical, biological and engineering systems. CRC Press, Boca Raton

    Google Scholar 

  • Barros LC, Bassanezi RC, Tonelli PA (2000) Fuzzy modelling in population dynamics. Ecological Modelling, 128: 27–33

    Article  Google Scholar 

  • Bartels F (1997) Ein Fuzzy-Auswertungs-und Krigingsystem für raumbezogene Daten. Diplomarbeit, Inst. für Informatik und Praktische Mathematik, Universität Kiel

    Google Scholar 

  • Bezdek JC (1980) A convergence theorem for the fuzzy c-means clustering algorithms. IEEE Trans. PAMI, PAMI-2(1): 1–8

    Google Scholar 

  • Bock W, Salski A (1998) A fuzzy knowledge-based model of population dynamics of the Yellow-necked mouse (Apodemus flavicollis) in a beech forest. Ecological Modelling, 108:155–161

    Article  Google Scholar 

  • Burrough PA, Macmillan RA, van Deursen (1992) Fuzzy classification methods for determination land suitability from soil profile observations and topography. Journal of Soil Science, 43: 193–210

    Google Scholar 

  • Burrough PA, van Gaans PFM, MacMillian RA (2000) High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems, 113: 37–52

    Article  Google Scholar 

  • Cheng Z, Horn DJ, Lindquist RK, Taylor RAJ (1996) Automated soil inference under fuzzy logic, Ecological Modelling, 90/2: 111–122

    Article  Google Scholar 

  • Cross V, Firat A (2000) Fuzzy objects for geographical information systems. Fuzzy Sets and Systems, 113: 19–36

    Article  Google Scholar 

  • Daunicht W, Salski A, Nöhr P, Neubert C (1996) A fuzzy knowledge-based model of the annual production of Skylarks. Ecological Modelling, 85: 67–74

    Article  Google Scholar 

  • Diamond P (1989) Fuzzy kriging. Fuzzy Sets and Systems, 33/3: 315–332

    Article  Google Scholar 

  • Dragicevic S, Marceau D J (2000) An application of fuzzy logic reasoning for GIS temporal modelling of dynamic processes. Fuzzy Sets and Systems, 113: 69–80

    Article  Google Scholar 

  • Enea M, Salemi G (2001) Fuzzy approach to the environmental impact evaluation. Ecological Modelling, 135: 131–147

    Article  Google Scholar 

  • Freyer B, Reisner Y, Zuberbühler D (2000) Potential impact model to assess agricultural pressure to landscape ecological functions. Ecological Modelling, 130:121–129

    Article  Google Scholar 

  • Friderichs M, Fränzle O, Salski A (1996) Fuzzy clustering of existing chemicals according to their ecotoxicological properties. Ecological Modelling, 85/1: 27–40

    Article  Google Scholar 

  • Guesgen HW, Albrecht J (2000) Imprecise reasoning in geographic information systems. Fuzzy Sets and Systems, 113: 121–131

    Article  Google Scholar 

  • Kampichler Ch, Barthel J, Wieland R (2000) Species density of foliage-dwelling spiders in field margins: a simple, fuzzy rule-based model. Ecological Modelling, 129: 87–99

    Article  Google Scholar 

  • Li B-L (ed.) (1996) Fuzzy Modelling in Ecology. Ecological Modelling, special issue, 90/2

    Google Scholar 

  • Li B-L (2001) Fuzzy statistical and modelling approach to ecological assessments. In: M. E. Jensen and P.S. Bourgeron (eds), A guidebook for integrated ecological assessments, Springer, New York: 211–220

    Google Scholar 

  • Liu W-Y, Song N (2001) The fuzzy association degree in semantic data models. Ecological Modelling, 117: 203–208

    Google Scholar 

  • Kacewicz M (1994) “Fuzzy” geostatistics — an intergration of qualitative discription into spatial analysis. In: Dimitrakopoulos, R., (Editor), Geostatistics for the next century, Kluwer Academic Publishers, Dordrecht: 448–463

    Google Scholar 

  • Kruse R, Gebhardt JG, Klawonn F (1995) Fuzzy-Systeme. Teubner, Stuttgart

    Google Scholar 

  • Marsili-Libelli S (1994) Fuzzy clustering of ecological data. Coenoses, 4/2: 95–106

    Google Scholar 

  • MacMillian RA, Pettapiece WW, Nolan SC, Goddard TW (2000) A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems, 113: 81–109

    Article  Google Scholar 

  • Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  • Pedrycz W (1996) Fuzzy modelling, Paradigms and practice. Kluwer Academic Publishers, Boston

    Google Scholar 

  • Piotrowski JA, Bartels F, Salski A, Schmidt G (1996) Geostatistical regionalization of glacial aquitard thicknessin northwestern Germany, based on fuzzy kriging., Mathematical Geology, 28/4: 437–452

    Article  Google Scholar 

  • Recknagel F, Petzoldt T, Jaeke O, Krusche F (1995). Hybrid expert system DELAQUA — a toolkit for water quality control of lakes and reservoirs. Ecol. Modelling 71, 1–3, 17–36

    Google Scholar 

  • Salski A (1992) Fuzzy knowledge-based models in ecological research. Ecological Modelling, 63: 103–112

    Article  Google Scholar 

  • Salski A, Kandzia P (1996) Fuzzy sets and fuzzy logic in ecological modelling. EcoSys, 4: 85–98

    Google Scholar 

  • Salski A, Fränzle O, Kandzia P (Editors) (1996) Fuzzy Logic in Ecological Modelling. Ecological Modelling, special issue, v. 85/1

    Google Scholar 

  • Salski A (1999) Ecological modelling and data analysis. In: H.-J. Zimmermann, Practical applications of fuzzy technologies (in: D. Dubois, H. Prade, The International Handbook of Fuzzy Sets Series, v.7), Kluwer: 247–266

    Google Scholar 

  • Salski A, Noell Ch (2001) Fuzzy linear programming for the optimization of land use scenarios. In: N. Mastorakis et al.(eds): Advances in Scientific Computing, Computational Intelligence and Applications, WSES Press: 355–360

    Google Scholar 

  • Shafer G, Pearl J (1990) Readings in uncertain reasoning. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  • Silvert W (1997) Ecological impact classification with fuzzy sets. Ecological Modelling, 96:1–10

    Article  Google Scholar 

  • Silvert W (2000) Fuzzy indices of environmental conditions. Ecological Modelling, 130:111–119

    Article  CAS  Google Scholar 

  • Wieland R, Schultz A, Hoffmann J (1996) The use of neural networks and fuzzy-methods in landscape modelling. Proc. of FUZZY’96, Zittau: 370–379

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Information and Control, 8: 338–353

    Article  Google Scholar 

  • Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Information Science, 8: 199–249

    Article  Google Scholar 

  • Zimmermann H-J (1993) Fuzzy Set Theory and its Applications. Kluwer Academic Publishers, Boston, Dordrecht, London, p. 399

    Google Scholar 

  • Zheng D (2001) A Neuro-fuzzy approach to linguistic knowledge acquisition and spatial decision making. PhD-Thesis, University of Vechta, p. 156

    Google Scholar 

  • Zhu A-X, Band LE, Dutton B, Nimlos TJ (1996) A semi-arid grazing ecosystem simulation model with probabilistic and fuzzy parameters. Ecological Modelling 90/2, 123–146

    Article  Google Scholar 

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Authors
  1. A. Salski
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Editors and Affiliations

  1. School of Earth and Environmental Sciences, The University of Adelaide, 5005, Australia

    Friedrich Recknagel

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© 2006 Springer-Verlag Berlin Heidelberg

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Salski, A. (2006). Ecological Applications of Fuzzy Logic. In: Recknagel, F. (eds) Ecological Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28426-5_1

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