Download Applications of Machine Learning to Ecological Modelling

Applications of Machine
Learning to Ecological Modelling
Saso Dzeroski
Jozef Stefan Institute
Ljubljana, Slovenia
Ecological modelling and
machine learning
The goals of modelling include
– understanding the domain studied
– predicting future values of system variables of interest
– decision support for environmental management
Machine learning can be used to
– automate modelling
– discover knowledge that meets some or all of the
above goals
Analysis of water quality data
• Biological classification
– British rivers
– Slovenian rivers
• Predicting chemical parameters of water quality
from bioindicator data
– British rivers
– Slovenian rivers
• Determining ecological requirements
of some organisms in Slovenian rivers
Modelling
• Modelling algal growth
– Lagoon of Venice
– Lake of Bled
Modelling phytoplankton growth
Modelling a red deer population
Environmental applications of
machine learning
Analysis of the influence of environmental factors
on respiratory diseases
Analysis of the influence of soil habitat features
on the abundance of Collembola
• Predicting biodegradability of chemical
compounds
• Runoff prediction from rainfall and past runoff
A regression tree for predicting
algal growth in the Venice lagoon
Rules for classifying British Midland rivers into quality classes
based on the community of benthic macroinvertebrates
IF
Hydrobiidae <= 3
AND Planorbidae <= 0
AND Gammaridae <= 5
AND Leuctridae > 0
THEN Class = B1a [42 0 0 0 0]
IF Asellidae > 2
AND 0 < Gammaridae <= 4
AND Scirtidae <= 0
THEN Class = B2 [0 0 41 0 0]
IF
Planariidae <= 0
AND Tubificidae > 0
AND Lumbricidae <= 0
AND Glossiphoniidae <= 2
AND Asellidae > 0
AND Gammaridae <= 0
AND Veliidae <= 0
AND Hydropsychidae <= 0
AND Simulidae <= 0
AND Muscidae <= 0
THEN Class = B3 [0 0 3 28 10]
Rate of change equation for phytoplankton growth
in Lake Glumsoe, Denmark
Variables in the model are the concentrations of:
• phytoplankton phyt
• zooplankton zoo
• soluble nitrogen nitro
• soluble phosphorus phosp
• water temperature temp
Analysis of environmental data
with machine learning methods
22-25 April 2002, Ljubljana
http://www-ai.ijs.si/SasoDzeroski/aep/
Introduction to machine learning
and its environmental applications
Data mining and knowledge discovery
Contents of course
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Induction of decision and regression trees
Induction of classification rules
Bayesian classification
Nearest neighbor classification
Evaluating, selecting and combining classifiers
Equation discovery
Practical hands-on exercises on environmental
datasets
• Applications of machine learning to
environmental problems
Recent applications
(joint work with participants from previous seminars)
Topics considered at workshops
• Modelling a red deer population (data cleaning,
body-weight model for calves of the year, two year olds and hinds)
• Influence of environmental and social factors on
acute respiratory diseases in children
• Influence of various parameters on alkalinity of an
artificial lake near an ashes dump
• Modelling the transport of concrete through pipes
Recent applications
(joint work with participants from previous seminars)
• Habitat-suitability modelling (using GIS data and
animal locations - sightings/radio-tracking)
– red deer (Debeljak et al. 1999)
– brown bears (A. Kobler and M. Adamič 1999): used to
identify locations for wildlife bridges across highways
• Influence on concentrations of dissolved reactive
phosphorus in surface runoff from arable land
(Weissroth and Džeroski 1999)
• Diagnosis of a waste-water treatment plant
(Džeroski and Comas 1999)