A data-integrated simulation model to forecast ground-level ozone concentration
Elevated ground-level ozone is hazardous to peoples health and destructive to the environment. This research develops a novel data-integrated simulation to forecast ground-level ozone (SIMGO) concentration based on a real data set collected from seven monitoring sites in the Dallas-Fort Worth area between January 1, 2005 and December 31, 2007. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for building the simulation. Classification and Regression Trees (CART), data mining tools for prediction and classification, were used to develop two tree structures in order to forecast ground-level ozone based on factors such as solar radiation and outdoor temperature. Kernel density estimation is used to estimate continuous distributions for the ground-level ozone concentration for seven days in advance. One week forecasts obtained from SIMGO for different months of a year is presented.
simulation ground-level ozone data mining