Forecasting of ozone episode days by cost-sensitive neural network methods.

Forecasting the occurrence of ozone episode days can be regarded as an imbalanced dataset classification problem. Since the standard artificial neural network (ANN) methods cannot make accurate predictions of such a problem, two cost-sensitive ANN methods, cost-penalty and moving threshold, were used in this study. The models classify each day as episode or non-episode according to the standard of daily maximum 8 h O(3) concentration. The ozone measurements from six monitoring stations in Taiwan were used for model training and performance evaluation. Two different input datasets, regional and single-site, were generated from raw air quality and meteorological observations. According to the numerical experiments, the predictions based on the regional dataset are much better than those obtained from the single-site dataset. Two cost-sensitive ANN methods were evaluated by receiver operating characteristic (ROC) curves. It was found that the results obtained by the two approaches are similar. If the misclassification costs are known, the cost-sensitive method can minimise the total costs. If the misclassification costs are unknown, the cost-sensitive ANN can obtain a better forecast than the standard ANN method when an appropriate cost ratio is used. For clean areas where episode days are very rare, the forecasts are poor for all methods.

Meteorologically adjusted ground level ozone trends in southern Taiwan.

Two methods were used to calculate the meteorologically adjusted ground level ozone trends in southern Taiwan. The first method utilized is a robust linear regression method. The second approach uses a multilayer perceptron (MLP) artificial neural network (ANN) method. The observations obtained from 16 monitoring stations were analyzed and divided into six groups by hierarchical divisive clustering procedure. The daily maximum 1 and 8 h ozone concentrations for each group are then calculated. The meteorologically adjusted trends obtained by linear regression and MLP methods are smaller than the unadjusted trends for all groups and average time. It indicts that the meteorological conditions in Taiwan tend to increase ambient ozone concentrations in recent years.