12
1.6
Conclusions
A. Salski
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 wh ich
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.
References
Ameskamp M (1997) Three-dimensional rule-based continuous soil modelling. Ph.D.
Thesis, Institut für Informatik und Praktische Mathematik, Christian- AlbrechtsUniversität, Kiel
Bärdossy A, Bogardi I, Kelly WE (1989) Geostatistics utilizing imprecise (fuzzy)
information. Fuzzy Sets and Systems, 31/3: 311-327
Bärdossy A, Bogardi I, Kelly WE (1990) Kriging with imprecise (fuzzy) variograms, I:
theory. Math. Geology, 22/3: 63-79
Bärdossy A, Duckstein L (1995) Fuzzy rule-based modeling with applications to
geophysical, biological and engineering systems. CRC Press, Boca Raton
Barros LC, Bassanezi RC, Tonelli PA (2000) Fuzzy modelling in population dynarnics.
Ecological Modelling, 128: 27-33
Barteis F (1997) Ein Fuzzy-Auswertungs- und Krigingsystem für raumbezogene Daten.
Diplomarbeit, Inst. für Informatik und Praktische Mathematik, Universität Kiel
Bezdek JC (1980) A convergence theorem for the fuzzy c-means clustering algorithms.
IEEE Trans. PAMI, PAMI-2(1): 1-8
Bock W, Salski A (1998) A fuzzy knowledge-based modelof population dynarnics of
the Yellow-necked mouse (Apodemus jlavicollis) in a beech forest. Ecological
Modelling, 108:155-161
Burrough PA, Macrnillan RA, Van Deursen (1992) Fuzzy classification methods for
determination land suitability from soil profile observations and topography. Journal
ofSoil Science, 43: 193-210
Burrough PA, van Gaans PFM, MacMillian RA (2000) High-resolution landform
classification using fuzzy k-means. Fuzzy Sets and Systems, 113: 37-52
Cheng Z, Horn DJ, Lindquist RK, Taylor RAJ (1996) Automated soil inference under
fuzzy logic, Ecological Modelling, 9012: 111-122
Cross V, Firat A (2000) Fuzzy objects for geographical information systems. Fuzzy Sets
and Systems, 113: 19-36
1.6
Conclusions
A. Salski
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 wh ich
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.
References
Ameskamp M (1997) Three-dimensional rule-based continuous soil modelling. Ph.D.
Thesis, Institut für Informatik und Praktische Mathematik, Christian- AlbrechtsUniversität, Kiel
Bärdossy A, Bogardi I, Kelly WE (1989) Geostatistics utilizing imprecise (fuzzy)
information. Fuzzy Sets and Systems, 31/3: 311-327
Bärdossy A, Bogardi I, Kelly WE (1990) Kriging with imprecise (fuzzy) variograms, I:
theory. Math. Geology, 22/3: 63-79
Bärdossy A, Duckstein L (1995) Fuzzy rule-based modeling with applications to
geophysical, biological and engineering systems. CRC Press, Boca Raton
Barros LC, Bassanezi RC, Tonelli PA (2000) Fuzzy modelling in population dynarnics.
Ecological Modelling, 128: 27-33
Barteis F (1997) Ein Fuzzy-Auswertungs- und Krigingsystem für raumbezogene Daten.
Diplomarbeit, Inst. für Informatik und Praktische Mathematik, Universität Kiel
Bezdek JC (1980) A convergence theorem for the fuzzy c-means clustering algorithms.
IEEE Trans. PAMI, PAMI-2(1): 1-8
Bock W, Salski A (1998) A fuzzy knowledge-based modelof population dynarnics of
the Yellow-necked mouse (Apodemus jlavicollis) in a beech forest. Ecological
Modelling, 108:155-161
Burrough PA, Macrnillan RA, Van Deursen (1992) Fuzzy classification methods for
determination land suitability from soil profile observations and topography. Journal
ofSoil Science, 43: 193-210
Burrough PA, van Gaans PFM, MacMillian RA (2000) High-resolution landform
classification using fuzzy k-means. Fuzzy Sets and Systems, 113: 37-52
Cheng Z, Horn DJ, Lindquist RK, Taylor RAJ (1996) Automated soil inference under
fuzzy logic, Ecological Modelling, 9012: 111-122
Cross V, Firat A (2000) Fuzzy objects for geographical information systems. Fuzzy Sets
and Systems, 113: 19-36