Forecasting of ground-level ozone concentrations in Taiwan by artificial neural network

• Main Authors: Wen-Tung Chen, 陳文通
• Other Authors: Hsu-Cherng Chiang
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/47058565776690600850

碩士 === 淡江大學 === 水資源及環境工程學系碩士班 === 94 === The ground-level ozone produced by photochemical air pollution is a serious environmental problem in Taiwan. It is a secondary pollutant generated by precursors (mainly NOx and VOC) through serial complicated reactions with other chemical species in the atmosphere. The forming of ozone is a complicated and non-linear reaction. Other than the chemical reaction, the weather condition, such as surface temperature, sunlight strength, cloudiness, wind speed, wind direction and humidity, also play an important role in the photochemistry pollution. This study focuses on building a model that predicts the next day’s maximun ozone concentration with artificial neural network. In the study, two forecasts, i.e., classical statistical and perfect prog forecast, were used to predict the next day’s maximum hourly and eight hour average ozone concentration prediction. The study found that the results obtained from perfect prog forecast are more accurate than that obtained from classical statistical forecast. The results of all forecasts are very reasonable. High correlations between the predictions of perfect prog forecast and measured 8h ozone concentrations are noted. However, all forecasts tend to overpredict in the low concentrations and underpredicted in the high concentrations.