title: Computational intelligence in earth sciences and environmental applications: issues and challenges.
abstract: This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application-domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research.
authors.affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. cherkass@ece.umn.edu
authors.forename: V
authors.initials: V
authors.lastname: Cherkassky
authors.forename: V
authors.initials: V
authors.lastname: Krasnopolsky
authors.forename: D P
authors.initials: DP
authors.lastname: Solomatine
authors.forename: J
authors.initials: J
authors.lastname: Valdes
mesh_terms: D001185:Artificial Intelligence; D002980:Climate; D003198:Computer Simulation; D018534:Earth, Planet; D004463:Ecology; D006801:Humans; D007360:Intelligence; D008962:Models, Theoretical