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Monday, August 26th – Opening session
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15:00
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Opening remarks
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15:20
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Keynote lecture
How many eco-sub-disciplines do we need?
Jørgensen S.E.
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16:20
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Ecological informatics: understanding ecology
by biologically-inspired computational techniques
Recknagel F.
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16:40
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Methodological issues in building, training,
and testing artificial neural networks
Ozesmi S.L., Tan C.O. and Ozesmi U.
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17:00
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Coffee break
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17:30
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Optimisation of predictive decision tree and
neural network ecosystem models with genetic algorithms
D'heygere T., Goethals P.L.M. and De Pauw N.
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17:50
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A framework for computer-based pattern recognition
and visualisation for the interpretation of ecological data
O'Connor M.A. and Walley W.J.
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18:10
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Projection Pursuit and robust indices for the
classification of ecological data
Werner H.
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18:30
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Improving neural network models by means of
theoretical ecological knowledge
Scardi M., Lek S., Park. Y.S., Verdonschot P. and Jorgensen
S.E.
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18:50
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Conference cocktail and dinner
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Tuesday, August 27th – Morning session
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9:00
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Keynote lecture
Learning metrics
Kaski S.
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10:00
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The structuring index: a tool for analysing
self-organizing maps
Giraudel J.L. and Lek S.
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10:20
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Knowledge discovery in two Australian stream
systems by means of Self-Organizing Maps and evolved rules
Horrigan N., Bobbin J. and Recknagel F.
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10:40
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Self-Organizing Mapping on response behavior
of indicator species exposed to toxic chemicals for developing
automatic bio-monitoring systems in aquatic environment
Chon T.S., Kwak I.S., Song M.Y., Ji C.W., Kim C.K., Cha E.Y.,
Koh S.C., Kim J.S., Leem J.B. and Lee S.K.
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11:00
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Coffee break
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11:30
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Collective phenomena in ecological time series
Lange H.
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11:50
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Application of genetic algorithms and Internet
computing to biodiversity science
Stockwell D., Beach J., Stewart A., Vorontsov G., Vieglais
D. and Scachetti Pereira R.
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12:10
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Modelling population and community dynamics
with Qualitative Reasoning
Salles P. and Bredeweg B.
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12:30
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Reduction of a complex biogeochemical model
with data mining techniques
Sperr T.A. and Wirtz K.W.
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12:50
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Lunch
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Tuesday, August 27th – PAEQANN
session #1
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14:30
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Tool for predicting aquatic ecosystem quality
using artificial neural networks (EU PAEQANN project)
Lek S., Bretin L.P., Coste M., Descy J.P., Ector L., Gevrey
M. , Giraudel J.L. , Knoflacher M., Jorgensen S.E., Park Y.S.
, Scardi M. and Verdonschot P.
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14:50
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Finding fish species patterns in the Garonne
basin (France) with a self-organising map
Aguilar Ibarra A., Park Y.S., Lim P. and Lek S.
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15:10
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Neural network modelling of freshwater fish
and macro-crustacean assemblages for biological assessment in
New Zealand
Joy M. K. and Death R.G.
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15:30
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Patterning and predicting fish assemblages
in large scale using artificial neural networks
Park Y.S., Lek S. and Oberdorff T.
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15:50
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Predicting
fish assemblages in rivers: a neural network case study
Scardi M., Cataudella S., Di Dato P., Maio G., Marconato
E., Salviati S., Tancioni L., Turin P. and Zanetti M.
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16:10
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Coffee break
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16:40
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Neural Network Patterning and Molecular Biological
Analysis of Fish Behavior as a Bio-monitoring System for Detecting
Toxic Chemicals in Environment
Shin S.W., Chon T.S., Cho H.D., Ji C.W., Choi W.S., Han J.Y.,
Kim J.S., Lee S.K. and Koh S.C.
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17:00
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Use of Artificial intelligence (Mir-max) for
Reference diatom communities definition in Rhone basin and Mediterranean
region (France)
Rimet F., Peeters V., Vidal H. and Ector L.
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17:20
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Identification and prediction of diatom assemblages
in rivers accross a range of environmental conditions in Europe:
case strudy of Belgium
Gosselain V., Campeau S., Gevrey M. and Fauville C.
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17:40
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Application of the Self Organizing Map algorithm
combined with the Structuring Index to study diatom assemblages
Tison J., Giraudel J.L., Coste M. and Delmas F.
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18:00
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Neural network modelling of diatom community
structure in the Loire river basin
Ector L., Rimet F., Di Dato P. and Scardi M.
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Wednesday, August 28th – Morning session
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9:00
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Keynote lecture
Information-based models of complex ecological processes
Hraber P.T.
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10:00
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A fuzzy logic model for fish recruitment forecast
Chen D.G.
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10:20
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An artificial neural network approach to model
fishermen search decisions and information exchange between
fishing vessels
Dreyfus-León M. and Gaertner D.
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10:40
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A comparison of various fitting techniques
for predicting yield for the Ubolratana reservoir (Thailand)
from a time series data on catch and hydrological features
Moreau J., Lek S., Leelaprata W., Sricharoendham B. and Villanueva
M.C.
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11:00
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Coffee break
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11:30
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River quality assessment based on fuzzy logic
Adriaenssens V., Goethals P.L.M. & De Pauw N.
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11:50
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Implementation of wavelets and artificial neural
networks to pattern recognition of response behaviors of Chironomids
(Chironomidae: Diptera) for water quality monitoring
Kim C.K., Kwak I.S., Cha E.Y. and Chon T.S.
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12:10
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Fuzzy expert system of water quality management
Frolova L.
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12:30
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A river pollution bayesian belief network (RPBBN)
for the diagnosis and prognosis of river health
Trigg D.J. and Walley W.J.
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12:50
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LIMPACT: An expert system to estimate the pesticide
contamination of small streams using benthic macroinvertebrates
as bioindicators
Neumann M. and Baumeister J.
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13:10
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Lunch
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Wednesday, August 28th – Afternoon session
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14:30
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Mapping the species richness and composition
of tropical forests from remotely sensed data with neural networks
Foody G.M. and Cutler M.E.
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14:50
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Extracting information from noisy survey data
on temporal change in vegetation following disturbance
Le Duc M.G., Pakeman R.J. and Marrs R.H.
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15:10
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Patterning forest structures from high resolution
LAI transects using Kohonen neural networks
Dubois M.A., Cournac L., Brosse S. And Park Y.S.
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15:30
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Spatial subgroup discovery applied to the analysis
of vegetation data
May M. and Ragia L.
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15:50
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Use of interactive forest growth simulation
to characterise stand structure
Parrott L. and Lange H.
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16:10
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A fuzzy approach to land suitability analysis
Salski A., Kandzia P. and Bartels F.
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| 16:30 |
Coffee
break |
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17:00
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Poster session
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18:00
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Conference excursion and dinner
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Thursady, August 29th – Morning session
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9:00
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Keynote lecture
Accuracy, Utility and Costs
Fielding A.H.
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10:00
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An independent test of an artificial neural
network model for predicting breeding success
Tan C.O., Ozesmi S.L., Ozesmi U. and Robertson R. J.
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10:20
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The generalizability of artificial neural network
models: the relationship between breeding success and occurrence
Ozesmi U., Ozesmi S.L., Tan C.O. and Robertson R.J.
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10:40
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An application of artificial neural networks
to carbon fluxes in three boreal streams
Holmberg M., Forsius M. and Starr M.
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11:00
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Coffee break
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11:30
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Exploring seasonal patterns using process modelling
and evolutionary
computation
Whigham P.A., Dick G. and Recknagel F.
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11:50
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Phytoplankton primary production in Chesapeake
Bay: a comparison between neural networks and other models
Harding L.W. Jr. and Scardi M.
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12:10
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A comparison of neural network and fuzzy logic
models for estimating seasonal pseudo steady state chlorophyll-a
concentrations in reservoirs
Chen D.G. and Soyupak S.
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12:30
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Applying Case-Based Reasoning to predict freshwater
phytoplankton dynamics
Whigham P.A. and Holt A.
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12:50
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Lunch
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Thursday, August 29th – PAEQANN
session #2
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14:30
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Comparing classical and modern modelling techniques
to predict macroinvertebrate community in the province of Overijssel
(The Netherlands)
Lek S., Gevrey M. , Giraudel J.L., Park Y.S., Scardi M. and
Verdonschot P.
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14:50
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Patterning on community dynamics of benthic
macroinvertebrates in streams by using the Self-Organizing Mapping
Kwak I.S., Song M.Y., Park Y.S., Cho H.D., Cha E.Y. and Chon
T.S.
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15:10
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Input variables selection of artificial neural
networks predicting aquatic macrobenthos communities in Flanders
(Belgium)
Gabriels W., Goethals P.L.M. and De Pauw N.
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15:30
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Developing modelling techniques for predicting
naturalness of Dutch streams
Nijboer R.C., Park Y.S., Lek S. & Verdonschot P.F.M.
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15:50
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A neural network approach to the prediction
of the benthic macroinvertebrate fauna composition in rivers
Di Dato P., Mancini L., Tancioni L. and Scardi M.
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16:10
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Coffee break
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16:40
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Patterning exergy of benthic macroinvertebrate
communities using artificial neural networks
Park Y.S., Lek S., Scardi M., Verdonschot P. and Jorgensen
S.E.
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17:00
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Development and assessment of fuzzy logic models
predicting aquatic macroinvertebrate taxa in the Zwalm catchment
Goethals P.L.M., Adriaenssens V., De Baets B. and De Pauw
N.
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17:20
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A macrofauna-environment based prediction model
using multinomial logistic regression
Verdonschot P.F.M., Goedhart P. and Nijboer R.C.
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17:40
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Predicting the functional structure of macroinvertebrate
communities in the Adour Garonne stream system (France)
Compin A., Park Y.S., Céréghino R., & Lek S.
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18:00
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Sensitivity and robustness of predictive neural
network ecosystem models for simulations of 'extreme' management
scenarios
Dedecker A., Goethals P.L.M. and De Pauw N.
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| 18:20 |
Prediction
of class membership by means of Support Vector Machines
Akkermans W. , Verdonschot P.F.M. and Nijboer R.C. |
Friday, August 30th – Closing session
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9:00
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Line transects: attempts to optimize sampling
efforts with the use of neural networks
de Thoisy B., Dubois M.A. and Brosse S.
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9:20
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Lake Ladoga Thermal Database: Design, Opportunities
And Results
Naumenko M. A. and Karetnikov S.G.
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9:40
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Forecast Estimation In A Soils
Koroleva T.
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10:00
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Environmental molding of human life history
evolution: modelling and data analysis
Teriokhin A.T., Thomas F., Renaud F., Budilova E.V. and Guégan
J.F.
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10:20
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Coffee break
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10:40
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Fisher information and dynamic regime changes in ecological
systems
Mayer A.L., Pawlowski C.W. and Cabezas H.
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11:00
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A new approach to determine the significance
of the two-way interaction in an artificial neural network model
Gevrey M. , Dimopoulos Y. and Lek S.
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11:30
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Application of information theory for ecological
interpretation of biological data
Knoflacher M.
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11:50
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Closing remarks
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Posters
- Creation and use of a database
of Dinophyta of Ukraine
Anishchenko I. and Krakhmalnyy A.
- Detection of microbiological
pollution in fresh water by fuzzy logic method
Bouharati S., Harzallah D., Benmahamed K., Abdesalem M. and Hachem
A.
- Development of a Decision
Support System for integrated
water management in the Zwalm river basin, Belgium
D’heygere T., Adriaenssens V., Dedecker A., Gabriels W., Goethals
P. and De Pauw N.
- Use of artificial neural networks and diatom
assemblages to predict rivers water quality
Gevrey M., Rimet F., Park Y.-S., Giraudel J.L., Ector
L. and Lek S.
- Fish diversity patterns in
rivers of the Garonne basin (France)
Ibarra A.A., Lim P., Belaud
A., Moreau J., Dauba F., Park Y.-S., Gevrey M. and Lek S.
- Evolving neural network algorithm
to freshwater ecological modelling: predicting phytoplankton blooms
in the lower Nakdong River (S. Korea)
Jeong K.S., Joo G.J., Kim H.W. and Cho G.I.
- Cyanobacterial dynamics in
the lower Nakdong River (S. Korea): pattern recognition of genus
shift using an unsupervised artificial neural network
Jeong K.S., Joo G.J., Kim D.K. and Ha K.
- PAEQANN project: Predicting
Aquatic Ecosystem Quality using Artificial Neural Networks. Impact
of Environmental characteristics on the Structure of Aquatic Communities
(Algae, Benthic and Fish Fauna).
Lek S., Coste M., Descy J.P., Ector L., Knoflacher M., Jorgensen
S.E., Scardi M. and Verdonschot P.
- Importance of the use of multivariate
analyses (AFC and ACPN) in structure studies of macroinvertebrates
in Zegzel-Cherraa river, Eastern Morocco
Maamri A.
- Development of methods for
understanding ecological data using self-organising map
Park Y.S., Chon T.S. and Lek S.
- Data mining and visualisation
in biological and environmental processes
Shanmuganathan S., Sallis P. and Buckeridge J.
- Cellular automata models applied
to landslides simulation on high performance computers
Spezzano G.
- BASIS, a case-based reasoning
system for lake management
van Nes E.H. and Scheffer M.
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