Abstract
The emerging field of Ecosystem Informatics applies methods from computer science and mathematics to address fundamental and applied problems in the ecosystem sciences. The ecosystem sciences are in the midst of a revolution driven by a combination of emerging technologies for improved sensing and the critical need for better science to help manage global climate change. This paper describes several initiatives at Oregon State University in ecosystem informatics. At the level of sensor technologies, this paper describes two projects: (a) wireless, battery-free sensor networks for forests and (b) rapid throughput automated arthropod population counting. At the level of data preparation and data cleaning, this paper describes the application of linear gaussian dynamic Bayesian networks to automated anomaly detection in temperature data streams. Finally, the paper describes two educational activities: (a) a summer institute in ecosystem informatics and (b) an interdisciplinary Ph.D. program in Ecosystem Informatics for mathematics, computer science, and the ecosystem sciences.
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Dietterich, T.G. (2007). Machine Learning in Ecosystem Informatics. In: Corruble, V., Takeda, M., Suzuki, E. (eds) Discovery Science. DS 2007. Lecture Notes in Computer Science(), vol 4755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75488-6_2
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DOI: https://doi.org/10.1007/978-3-540-75488-6_2
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