Case Based Classifier

Elias Kalapanidas and Nikolaos Avouris* University of Patras Electrical and Computer Engineering Department GR-265 00 Rio Patras, Greece email: {ekalap , N.Avouris } @ ee.upatras.gr * Corresponding Author Abstract 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 (CART). The overall performance of NEMO makes it a good candidate to support air pollution experts in operational conditions.