SHORT-TERM OZONE FORECASTING BY ARTIFICIAL NEURAL NETWORKS

In this work we report preliminary results of a study aiming to develo p an intelligent tool for performing ozone forecasting in the polluted atmosphere of Mexico City. This tool is based in the paradigm of neur al networks. Two neural models are used in this work, namely, the Bidi rectional Associative Memory (BAM) and the Holographic Associative Mem ory (HAM). We analyse and preprocess daily patterns of meteorological variables and concentrations of pollutants as measured by five monitor ing stations in Mexico City. These patterns are used to train both neu ral networks and then we use them to predict ozone at one point in the city. Preliminary results are reported and some conclusions are drawn .