Fuzzy goal programming approach for water quality management in a river basin
Fuzzy Sets and Systems
Artificial Intelligence and Environmental Decision Support Systems
Applied Intelligence
Development of a Decision Support for Integrated Water Management in River Basins
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
FRAME: a Knowledge-based Tool to Support the Choice of the Right Air Pollution Model
CSEIA '93 Proceedings of the IFIP TC5/WG5.11 Working Conference on Computer Support for Environmental Impact Assessment
Exploring the ecological status of human altered streams through Generative Topographic Mapping
Environmental Modelling & Software
Kohonen self-organizing maps and mass balance method for the supervision of a lowland river area
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
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Nowadays, bad river water quality has become a serious problem, especially in developed regions, due to the high nutrient loads from anthropogenic sources dumped into the rivers. Pollution sources can have different origins: point or non-point sources. As point sources can be well identified, these can be controlled, but the identification and control of non-point sources is not an easy task. Moreover, the natural responses of polluted streams in front of these external aggressions are still quite unknown. The decision-making processes involved in stream reach management require extensive human expertise (from water managers), empirical knowledge from scientific research and elaborated calculation over large amounts of numerical and symbolic data. In this sense, the STREAMES project appears as an attempt to develop and implement a knowledge-based decision support system to help water managers in taking decisions. The knowledge acquisition process is the most important step to build a complete knowledge base. After acquiring the knowledge, the efforts will concentrate on structuring and representing the knowledge in a decision tree fashion as a previous step to build the knowledge base. Each decision tree developed refers to a specific river problem: eutrophication, excess of ammonia, organic matter pollution … This paper presents the STREAMES project, with major emphasis on the knowledge acquisition step.