Computational intelligence in earth sciences and environmental applications: Issues and challenges Export

@article{citeulike:2602176, 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[hyphen (true graphic)]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.}, author = {Cherkassky, V. and Krasnopolsky, V. and Solomatine, D. P. and Valdes, J.}, booktitle = {Earth Sciences and Environmental Applications of Computational Intelligence}, citeulike-article-id = {2602176}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neunet.2006.01.001}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6T08-4JF97F1-2/2/d635e27828a5c41613cb2308ad662b7a}, doi = {10.1016/j.neunet.2006.01.001}, journal = {Neural Networks}, keywords = {data-reduction, forecasting, genetic-algorithm, genetic-programming, hydroinformatics, neural-networks, optimization}, month = {March}, number = {2}, pages = {113--121}, posted-at = {2008-03-27 14:33:02}, priority = {4}, title = {Computational intelligence in earth sciences and environmental applications: Issues and challenges}, url = {http://dx.doi.org/10.1016/j.neunet.2006.01.001}, volume = {19}, year = {2006} }

TY - JOUR ID - citeulike:2602176 L3 - citeulike-article-id:2602176 N2 - 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[hyphen (true graphic)]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. IS - 2 JF - Neural Networks EP - 121 TI - Computational intelligence in earth sciences and environmental applications: Issues and challenges VL - 19 T2 - Earth Sciences and Environmental Applications of Computational Intelligence SP - 113 KW - data-reduction KW - forecasting KW - genetic-algorithm KW - genetic-programming KW - hydroinformatics KW - neural-networks KW - optimization AU - Cherkassky, V AU - Krasnopolsky, V AU - Solomatine, DP AU - Valdes, J PY - 2006/03// UR - http://dx.doi.org/10.1016/j.neunet.2006.01.001 ER -