Journal
NEURAL NETWORKS
Volume 19, Issue 2, Pages 113-121Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2006.01.001
Keywords
neural networks; predictive learning; earth sciences; environment; climate; hydrology
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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 follow by discussion of open issues and directions for future research. (c) 2006 Elsevier Ltd. All rights reserved.
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