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Comparative analysis of ozone level prediction models using gene expression programming and multiple linear regression

机译：使用基因表达程序和多元线性回归的臭氧水平预测模型的比较分析

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ground-level ozone (O3) has been a serious air pollution problem for several decades and in many metropolitan areas, due to its adverse impact on the human respiratory system. Therefore, to reduce the risks of O3 related damages, developing, maintaining and improving short term ozone forecasting models is needed. This paper presents the results of two prognostic models including gene expression programming (gEP), which is a variant of genetic programming (gP), and multiple linear regression (MLR) to forecast ozone levels in real-time up to 6 hours ahead at four stations in Bilbao, Spain. The inputs to the gEP were meteorological conditions (wind speed and direction, temperature, relative humidity, pressure, solar radiation and thermal gradient), hourly ozone levels and traffic parameters (number of vehicles, occupation percentage and velocity), which were measured in the years of 1993–94. The performances of developed models were compared with observed values and were evaluated using specific performance measurements for the air quality models established in the Model Validation Kit and recommended by the US Environmental Protection Agency. It was found that the gEP in most cases gives superior predictions. Finally it can be concluded on the basis of the results of this study that gene expression programming appears to be a promising technique for the prediction of pollutant concentrations.
机译：几十年来，由于其对人体呼吸系统的不利影响，地面臭氧（O3）一直是一个严重的空气污染问题。因此，为了减少与O3有关的损害的风险，需要开发，维护和改进短期臭氧预测模型。本文介绍了两种预后模型的结果，其中包括基因表达编程（gEP）（这是遗传编程（gP）的一种变体）和多元线性回归（MLR）来实时预测臭氧水平，该预测可以在4点之前提前6小时进行西班牙毕尔巴鄂的加油站。 gEP的输入是气象条件（风速和风向，温度，相对湿度，压力，太阳辐射和热梯度），每小时的臭氧水平和交通参数（车辆数量，占用百分比和速度），在1993-94年。将已开发模型的性能与观测值进行比较，并使用模型验证工具包中建立的，由美国环境保护局推荐的空气质量模型的特定性能测量结果进行评估。已经发现，在大多数情况下，gEP给出了更好的预测。最后，根据这项研究的结果可以得出结论，基因表达程序设计似乎是一种预测污染物浓度的有前途的技术。

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