Discovering numeric laws, a case study: CO₂ fugacity in the ocean

Article type: Research Article

Authors: Wickramaratna, Kasun^{a; *} | Kubat, Miroslav^a | Minnett, Peter^b

Affiliations: [a] Department of Electrical and Computer Engineering University of Miami, MI, USA | [b] Rosenstiel School of Marine and Atmospheric Science University of Miami, MI, USA

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: The idea to automate the discovery of numeric laws goes back to early 1980's when some authors showed how to use to this end AI search techniques. Later, the community somewhat lost interest in this task, reasoning that it was unlikely that a computer program would ever "outperform" human intuition supported by background knowledge. Only recently did some authors manage to overcome this scepticism: their programs were able to discover new numeric laws in soft-science domains such as ecology and psychology. In the case study reported here, we describe a successful attempt to assist ocean chemists in their attempts to predict absorption rate of CO2 in certain regions of the ocean. The system we have developed has found equations that outperform those suggested by field experts assisted by regression techniques. Our experience indicates that more attention should be paid to the following: the impact of spatio-temporal aspects, the existence of "hidden causes" (not directly reflected in a given set of variables), and the need for a field expert to post-process the results.

Keywords: Quantitative law discovery, genetic algorithm, ocean chemistry

DOI: 10.3233/IDA-2008-12405

Journal: Intelligent Data Analysis, vol. 12, no. 4, pp. 379-391, 2008