Present Trends in the Application of Artificial Intelligence to Environmental Systems

1. Present Trends in the Application of Artificial Intelligence to Environmental Systems Account of 3rd Conference on AI Applications to Environmental Science

2. AI Techniques/Disciplines • Genetic Algorithms • Neural Networks • Support Vector Machines (generalized version of NNs for image analysis) • Expert Systems • Fuzzy Logic

3. Genetic Algorithms • GA’s are being used for a number of environmental applications • Calibrating water quality models • Managing groundwater • Astrophysics • Source of air pollutant • Predatory-Prey model • Inverse problems in general

4. Genetic Algorithms (2) • Key to application to Environmental Science problems is to set the GA as an optimization problem • Non linear differential equations can be approached using GA

5. Neural Networks • Increasingly used by NCEP/NOAA/NASA and European Agencies to speed up computationally expensive tasks (NN can be orders of magnitude faster than classic algorithms) • Use of NNs to correct model biases • Use of NNs for predictive learning (system imitation)

6. Neural Networks (2) • NN for predictive learning: choosing the “best” model for a given loss function • “Do not solve a given problem by indirectly solving a more general and more complicated problem as an intermediary step” • Possible problems for NNs when the size of the problems become to large (atmospheric models)

7. General NN Techniques • Our techniques are very much in line with other modelers (choice of model parameters, normalization, model evaluation, etc.) • Use of PCA analysis to identify smaller more optimum input decks • Use of NNs for non linear PCA analysis

8. Discussion Item(Caren Marzban) • Recognition that the parameters of AI models generally do not have a physical significance • In particular there is no physical meaning in the biases and weights of a neural network • Question/Hypothesis: The parameters of any non linear models do not provide insight into the physics of the system

9. Support Vector Machines • Relatively new technique which vectorizes pictures/maps and uses a NN type methodology to extract features • uses kernel functions • Also kernel PCA, kernel Gram-Schmidt, Bayes Point Machines, Relevance and Leverage Vector Machines, are some of the other algorithms that make crucial use of kernels for problems of classification, regression, density estimation, novelty detection and clustering

10. Support Vector Machines (2) • For classification, SVMs operate by finding a hypersurface which attempts to split the positive examples from the negative examples. The split will be chosen to have the largest distance from the hypersurface to the nearest of the positive and negative examples.

11. http://www.computer.org/intelligent/ex1998/pdf/x4018.pdf

12. http://www.computer.org/intelligent/ex1998/pdf/x4018.pdf

13. General Issues • AI techniques are increasingly used to make short to medium term forecasts (sometimes fractions of hours) when large amounts of data are available • Usual forecasting methodologies have trouble for short term forecasts • Harris Corporation is implementing a ceiling and visibility fuzzy logic tool for the FAA

14. Groundwater Issues • Population growth and possible global change affects/will affect potable water supply including groundwater supply • Decision making is at the (very) local level and relatively uncoordinated (likelihood of conflicts) • It takes 20-25 years to go from the planning to the realization of a new reservoir

15. Bush Administration on Climate Change • Global climate change recognized as a capstone issue of our generation by the Bush administration • Climate change program ~ 1.7 bio/year • 4 part focus: • Science • Observation and Data • Decision support resources • Outreach and Education • Core solutions to the problem will be technological breakthroughs

16. Scatterometry Overview: OceanCirculation Scatterometer high resolution, extensive, and frequent wind velocity measurements are used to understand upper ocean circulation from regional to global scales • Wind stress is the largest momentum • input to ocean • Wind stress curl drives large-scale • upper ocean currents • Small-scale wind variability modifies • large-scale ocean circulation • Coastal regions exhibit amplified • physical/biological response • Wind forcing complements dynamic • and thermodynamic response • measurements Winds & Water, Weather & Waves: Satellite Scatterometry, M.H. Freilich, AMS Short Course, February, 2003

17. Scatterometry Overview: Meteorology All-weather surface wind measurements provide unique information for atmospheric research and operations Winds & Water, Weather & Waves: Satellite Scatterometry, M.H. Freilich, AMS Short Course, February, 2003

18. Hurricane Dora QuikSCAT -- 10 August 1999 Global backscatter and ocean vector winds < 11 hour coverage M. H. Freilich COAS/OSU W. L. Jones UCF Winds & Water, Weather & Waves: Satellite Scatterometry, M.H. Freilich, AMS Short Course, February, 2003

19. WIND MEASUREMENTS: Mission Schedules