Predicting daily ozone concentration maxima using fuzzy time series based on a two-stage linguistic partition method

TLDR: Two new fuzzy time series based on a two-stage linguistic partition method to predict air quality with daily maximum O 3 concentration show a greatly improved performance in daily maximal ozone concentration prediction accuracy compared with the other models.

Abstract: Air pollution is a result of global warming, greenhouse effects, and acid rain. Especially in highly industrialization areas, air pollution has become a major environmental issue. Poor air quality has both acute and chronic effects on human health. The detrimental effects of ambient ozone on human health and the Earth’s ecosystem continue to be a national concern in Taiwan. The pollutant standard index (PSI) has been adopted to assess the degree of air pollution in Taiwan. The standardized daily air quality report provides a simple number on a scale of 0 to 500 related to the health effects of air quality levels. The report focuses on health and the current PSI subindices to reflect measured ozone (O 3 ) concentrations. Therefore, this study uses the O 3 attribute to evaluate air quality. In an effort to forecast daily maximum ozone concentrations, many researchers have developed daily ozone forecasting models. However, this continuing worldwide environmental problem suggests the need for more accurate models. This paper proposes two new fuzzy time series based on a two-stage linguistic partition method to predict air quality with daily maximum O 3 concentration: Stage 1, use the fuzzy time series based on the cumulative probability distribution approach (CPDA) to partition the universe of discourse into seven intervals; Stage 2, use two linguistic partition methods, the CPDA and the uniform discretion method (UDM), to repartition each interval into three subintervals. To verify the forecasting performance of the proposed methods in detail, the practical collected data is used as and evaluating dataset; five other methodologies (AR, MA, ARMA, Chen’s and Yu’s) are used as comparison models. The proposed methods both show a greatly improved performance in daily maximal ozone concentration prediction accuracy compared with the other models.