FUZZEKS [to Index]
General overview
The Fuzzy Evaluation and Kriging System FUZZEKS can be
used for
- spatial (2-dimensional) interpolation (also called regionalization) and
- aggregation of different parameters (before the interpolation)
Both procedures utilize both measurements and fuzzy input data.
The regionalization part can be used without the aggregation part.
Spatial interpolation
The spatial interpolation is done by fuzzy kriging, a modified
kriging method that allows to use fuzzy numbers
as input data.
Kriging (non-fuzzy) is a common interpolation method, which
bases on a statistical analysis of input data, precisely
on experimental variograms, which can be handled by FUZZEKS.
An advantage of kriging is that an estimation variance can be calculated
for the kriging results.
It can help in the interpretation of the kriging results.
If fuzzy input data are used, the fuzzyness of the kriging result
also helps in its interpretation
(as discussed below in the chapter about applications).
Aggregation of different parameters
If more parameters should be taken into account for regionalization,
they can be aggregated before kriging.
In order to aggregate these parameters they have to be transformed to
a common scale (by the so-called membership functions) and then
be combined by different operators.
Example: In order to get membership values of a given area for
some quality such as "suitable as waste disposal site" some soil parameters
(such as clay content, Cl-concentration, the thickness of some layer, etc.)
must be transformed by approriate membership functions.
Then they are on a common scale and can be combined using functions like
"weighted sum" (and others).
These aggregation facilities also allow fuzzy numbers as input.
Display and output of results
FUZZEKS offers many display-options to view the results
in 2 spatial dimensions (such as isolines, greyshades and colors) and
FUZZEKS also allows to view the results for a part of the area only
(along a cut/section and for a point).
ASCII-representations are available too.
Applications
Land evaluation (e.g. soil quality of a given area with respect to
suitability for growing
maize) is one possible application field [Burrough, 1989] (References are
at the bottom of this page).
FUZZEKS has alredy been used in the field of Geology (to perform
fuzzy kriging of hydrogeological data
[Piotrowski et al., 1994, 1995, 1996]).
Other applications are planned.
The simplified example demonstrates an
examination of suitability as waste disposal site.
There are many advantages in using fuzzy input data. An example is
the possibility to incorporate expert knowledge (can be used for the
definition of
fuzzy numbers) in places where exact measurements are rare in order
to reduce the kriging variance.
As the kriging variance decreases, the fuzziness appears in the result.
At the first glance this
may look like no advantage in the end. But the result now presents more
information, because the vague information is taken into account
that could not be used by conventional methods.
Another point is that data often incorporate fuzziness quite naturally,
as e.g. measurement tolerances (which can be expressed as fuzzy numbers).
It can be of great advantage to know about the result's tolerances when
the results have to be judged. If the fuzziness is not taken into account,
results can not be judged as accurately.
Tips on how to read this documentation and
description of window types
In order to get a first impression of FUZZEKS and its facilities,
examination of the
simplified example
- which demonstrates most of the facilities - is strongly recommended.
If you like to see quickly how FUZZEKS can be used in order to do
fuzzy kriging only, look at
"Quick start: Fuzzy kriging".
Once you are used to the window concept of FUZZEKS, the
"Quick reference / Window description" section of the
table of contents can be employed to find a topic in this help text
in a very fast way:
First select the window type you are interested in. You'll get a page with a
picture of a typical window of that type. Then you can simply select
the item you want information on with the mouse.
In order to simplify the use of this feature (and also to help understanding
the simplified example),
an overview of FUZZEKS window-types follows.
- The background window
is used for general purposes such as: defining where data should be
stored, loading input data, and exiting the program.
After defining where data should be stored the user does not need to
save explicitily, because the
program updates the files automatically (when changes are detected).
- The management and composition window
is used as central management component. If you want to open or find
any other window of FUZZEKS, simply press F6 in order to switch to
the management window. Clicking at the appropriate item opens or
activates the corresponding window.
The tools to define the aggregation of the parameters are also
located in this window.
- Membership function windows
are used to define a transformation that is needed as preparation
to aggregate different parameters.
- Kriging windows are used to deal with
variograms and the display of fuzzy kriging results.
How to get help on a specific topic describes all
facilities to find an appropriate help page.
The simplest ways to do it is
- to seek through the "Detailed system description index"
section in the table of contents or
- to search for a keyword by selecting the appropriate button at the
top of the Windows-help window.
References
- A. Bárdossy, I. Bogardi, and W.E.Kelly:
"Geostatistics utilizing imprecise (fuzzy) information",
Fuzzy Sets and Systems, vol.31, pp.311-327, 1989
Fuzzy kriging (using fuzzy and crisp variograms)
- P. A. Burrough:
"Fuzzy mathematical methods for soil survey and land evaluation",
Journal of Soil Science, 1989, 40, pp.477-492
Composition of (crisp) soil parameters using membership
function ("Semantic Import Model") as preparation and weighted
sum as composition
- L. Zadeh:
"Fuzzy Sets",
Information and Control 8, 1965; pp.338-353
Often referenced in papers about fuzzy sets,
Extension Principle is introduced
- J. A. Piotrowski, F. Bartels, A. Salski, G. Schmidt (1994):
"Fuzzy Kriging of Imprecise Hydrogeological Data",
International Association for Mathematical Geology, Annual Conference
Mont Tremblant, Quebec, Canada, October 1-5, 1994; Proceedings:
pp.282-288
First application of the fuzzy kriging part of FUZZEKS
- J. A. Piotrowski, F. Bartels, A. Salski, G. Schmidt (1995):
"Fuzzy logic in hydrogeology - closer to nature?",
9 Int. Conf. on the state of the Art of Ecological Modelling (ISEM'95),
11-15 August 1995, Beijing, China; Abstracts: S.91.
- J. A. Piotrowski, F. Bartels, A. Salski, G. Schmidt (1995):
"Geostatistische Regionalisierung hydrogeologischer Parameter
mit Fuzzy Kriging",
62. Tagung der Arbeitsgem. Nordwestdeutscher Geologen,
Hamburg-Bergedorf, Tagungsband, 12-19
- J. A. Piotrowski, F. Bartels, A. Salski, G. Schmidt (1996):
"Fuzzy-Kriging-Regionalisierung hydrogeologischer Parameter",
In: Merkel, B., Dietrich, P.G., Struckmeier, W. & L Löhnert, E.P. (Hrsg.):
Grundwasser und Rohstoffgewinnung. GeoCongress 2, Verlag Sven von Loga,
Köln, 400-405.
- J. A. Piotrowski, F. Bartels, A. Salski, G. Schmidt (1996):
"Geostatistical regionalization of glacial aquitard thickness in
northwestern Germany, based on fuzzy kriging",
Mathematical Geology 28(4): 437-452.
- J. A. Piotrowski, F. Bartels, A. Salski, G. Schmidt (1996):
"Estimation of hydrogeological parameters for groundwater modelling
with fuzzy geostatistics: closer to nature?",
In: Kovar, K. & van der Heijde, P. (eds.) Calibration and Reliability
in Groundwater modelling; Int. Conf. ModelCARE'96,
Golden (Colorado), USA, 24.-26. Sept. 1996; Proceedings, 511-520.
- F. Bartels:
"Entwicklung eines Fuzzy-Auswertungs- und Krigingsystems für
raumbezogene Daten",
M.Sc. thesis, Institute of Informatics, University of Kiel (in German)
The most detailed and complete theoretical documentation of
FUZZEKS