title: [Application of artificial neural networks on the prediction of surface ozone concentrations].
abstract: Ozone is an important secondary air pollutant in the lower atmosphere. In order to predict the hourly maximum ozone one day in advance based on the meteorological variables for the Wanqingsha site in Guangzhou, Guangdong province, a neural network model (Multi-Layer Perceptron) and a multiple linear regression model were used and compared. Model inputs are meteorological parameters (wind speed, wind direction, air temperature, relative humidity, barometric pressure and solar radiation) of the next day and hourly maximum ozone concentration of the previous day. The OBS (optimal brain surgeon) was adopted to prune the neutral work, to reduce its complexity and to improve its generalization ability. We find that the pruned neural network has the capacity to predict the peak ozone, with an agreement index of 92.3%, the root mean square error of 0.0428 mg/m3, the R-square of 0.737 and the success index of threshold exceedance 77.0% (the threshold O3 mixing ratio of 0.20 mg/m3). When the neural classifier was added to the neural network model, the success index of threshold exceedance increased to 83.6%. Through comparison of the performance indices between the multiple linear regression model and the neural network model, we conclud that that neural network is a better choice to predict peak ozone from meteorological forecast, which may be applied to practical prediction of ozone concentration.
authors.affiliation: Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, China. sllhappy0729@163.com
authors.forename: Lu-Lu
authors.initials: LL
authors.lastname: Shen
authors.forename: Yu-Xuan
authors.initials: YX
authors.lastname: Wang
authors.forename: Lei
authors.initials: L
authors.lastname: Duan
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data; D001272:Atmosphere / Q000737:chemistry*; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D006801:Humans; D008685:Meteorological Concepts; D016571:Neural Networks, Computer; D010126:Ozone / Q000032:analysis*; D012044:Regression Analysis
chemical_list: D000393:Air Pollutants; D010126:Ozone