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Abstract
Environmental monitoring is usually based on large volumes of data, while in general, environmental decision making is a complex problem, has a high degree of uncertainty, and involves diverse areas of expertise. Environmental decision-support systems are therefore good candidates for application of artificial intelligence (AI) techniques. In this paper it is argued that a suitable approach for building these systems is the use of case-based reasoning or analogical reasoning techniques, which offer more adaptability and better explanation facilities than other AI paradigms. As an example, the development stages, the architecture, and the operational characteristics of the expert system Air Quality Predictor (AIRQUAP), developed to predict air pollution levels in Athens, Greece, are described. AIRQUAP helps users retrieve historical data intelligently and can predict air pollution levels, useful for management of air pollution episodes. The performance of the system is also compared with other techniques used in this class of applications.
