Applications of Machine Learning to Ecological Modelling

1. Applications of Machine Learning to Ecological Modelling Saso Dzeroski Jozef Stefan Institute Ljubljana, Slovenia

2. 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

3. 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

4. Modelling • Modelling algal growth • Lagoon of Venice • Lake of Bled Modelling phytoplankton growth Modelling a red deer population

5. 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

6. A regression tree for predicting algal growth in the Venice lagoon

7. Rules for classifying British Midland rivers into quality classes based on the community of benthic macroinvertebrates 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] 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]

8. 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

9. 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

10. Contents of course • 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

11. 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

12. 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)