Integrating empirical and heuristic knowledge in a KBS to approach stream eutrophication.

Abstract

The nutrient enrichment of rivers and its consequences are among the most severe water quality problems in Europe, causing eutrophication and other problems. The decision-making processes involved in the management of these problems require extensive human expertise from people who deal directly with day-to-day stream problems, as well as empiricalknowledge based on scientific research. This means that eutrophication is a complex problem, the optimal management of which requires an integrated and multidisciplinary approach. This approach can be taken using aKnowledge-Based System (KBS) built upon the concepts and methods of human reasoning. Accordingly, aKBS was developed within the STREAMES project. In this KBS most of the knowledge needed for managing eutrophication problems was organised and structured in the form of a decision tree (DT). The methodology specially developed to build this KBS, as well as the internal structure of the eutrophication decision tree, is presented here. The good DT obtained led to consider the KBSa suitable tool to support the management of eutrophication.