Computational intelligence in earth sciences and environmental applications: issues and challenges

Neural Networks : the Official Journal of the International Neural Network Society
V CherkasskyJulio J Valdés

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.

References

Apr 4, 2003·Neural Networks : the Official Journal of the International Neural Network Society·Vladimir M Krasnopolsky, Helmut Schiller
Apr 4, 2003·Neural Networks : the Official Journal of the International Neural Network Society·Vladimir M Krasnopolsky, Frédéric Chevallier
Aug 27, 2005·IEEE Transactions on Neural Networks·Vladimir Cherkassky, Yunqian Ma

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Citations

Sep 18, 2012·Environmental Monitoring and Assessment·Getnet D BetrieRehan Sadiq
Jun 10, 2011·International Journal of Neural Systems·J P FloridoI Rojas
Jan 1, 2013·Neural Networks : the Official Journal of the International Neural Network Society·Laurent BourgoisMohammed Benjelloun

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