Short-term air quality prediction using a case-based classifier

In the frame of air quality monitoring of urban areas the task of short-term prediction of key-pollutants concentrations is a daily activity of major importance. Automation of this process is desirable but development of reliable predictive models with good performance to support this task in operational basis presents many difficulties. In this paper we present and discuss the NEMO prototype that has been built in order to support short-term prediction of NO2 maximum concentration levels in Athens, Greece. NEMO is based on a case-based reasoning approach combining heuristic and statistical techniques. The process of development of the system, its architecture and its performance, are described in this paper. NEMO performance is compared with that of a back propagating neural network and a decision tree. The overall performance of NEMO makes it a good candidate to support air pollution experts in operational conditions.