Data Mining for Environmental Systems

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 modelling 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 modelling 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 chapter is a general introduction to Data Mining techniques for Environmental Scientists who may be interested in using them in their applications. So the presentation focuses on the contributions of data mining techniques to environmental applications and on general guidelines of good practice in real world domains. The purpose of this chapter is not to present technical details on specific data mining techniques, but rather to provide general guidance to non-expert users to help them decide which technique is appropriate for solving their problem. References to the wider literature are provided.