Knowledge Discovery in Environmental Data.

An approach to tackling management problems of water resources is the introduction of the advanced ICTs (Information and Communication Technologies). In IWM (Integrated Water Management) and, in general, in the environmental field the power of these technologies has allowed for large-scale data collection campaigns. The number of parameters that must be measured to monitor an ecosystem is potentially high. Systematic measurement of those parameters generates huge amounts of data that should be suitably interpreted and used. A pragmatic approach has to be used to get the best information = knowledge from all this data. Within such amounts of data there is a lot of hidden information, in terms of models, patterns and trends. But information is difficult to be extracted since data is of varying quantity and quality. As a consequence, semi-automatic knowledge extraction from data has gained great importance within the economic and scientific community. Knowledge Discovery from Databases (KDD) has emerged as a framework where a plethora of techniques for identifying useful and understandable patterns in data have flourished. Most of those techniques can be used with success in the environmental field and, in particular, in IWM. In this paper we give an overview of what KDD is and mention some applications in these areas. Keywords: Integrated Water Management, Data Mining, Environmental Databases [ABSTRACT FROM AUTHOR]