Data mining for environmental systems
Abstract
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modeling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modeling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. The paper is a general introduction to Data Mining techniques for Environmental Scientists who may be interested in using them in their applications. So, special work is done on the contributions of DM techniques to environmental applications and on general guidelines of good practice in real world domains. Technical details on the specific DM techniques are not the focus of this paper, but to provide general ideas to a non expert user that reading them can decide which is the proper technique useful to solve his problem and specific references are provided
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Published as:
K. Gibert,
J. Spate,
M. Sánchez-Marré,
I. N. Athanasiadis,
J. Comas,
Data mining for environmental systems,
Environmental Modelling, Software and Decision Support: State of the art and new perspective, Developments in Integrated Environmental Assessment, vol. 3, pg. 205-228,
2008, Elsevier, doi:10.1016/S1574-101X(08)00612-1.
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