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A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks

机译：一个基于规则跳跃神经网络的循环水库每日臭氧质量浓度预报的新颖框架

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摘要

An understanding of the growth in surface concentration of ozone and its adverse health effects are important for environmental departments to make sensible decisions for future. Our hybrid model CEEMD+CRJ+MLR is the first attempt to improve CRJ in the field of air pollution prediction and ozone forecasting. For this novel framework, CEEMD has been adopted to decompose original MDA8_O-3 history into several sub-series. After that, for each IMF, CRJ is used to extract time-series features. These time-series features are fed into appropriate machine learning methods for prediction. In addition to that, residual is also predicted through normal methods. A model, which is trained with data from 1 May 2014 to 31 May 2017, is validated with data from 1 June 2017 to 30 May 2018, obtained from four stations of Beijing, China. The hybrid model has input variables which are combined with related pollutants, meteorological forecasts and UV index, and predict maximum daily 8-h average ozone (MDA8_O-3) concentration in different time intervals. Our experimental results show that the CEEMD+CRJ+MLR model exhibits the best performance compared with other benchmark models generally. For four stations, IA, MAE, RMSE and MAPE average of +1 (forecasting 1 day in advance) are 0.9763, 12.84, 17.81 and 18.5% respectively and of +2 are 0.9679, 15.17, 20.15 and 23.86% respectively. Especially in the case of forecasting heavy ozone concentration (Level III), a critical issue in air pollution predictions, the classification rate of our hybrid model has improved from 29.4% (for CRJ) to 83.4% in +1 and from 38% (for CRJ) to 73% in +2. For long time forecasting, the CEEMD+CRJ+MLR also shows its outstanding performance in whole levels and level III ozone concentration. Our hybrid model, with accurate and stable results, is highly effective for MDA8_O-3 concentration prediction and can efficiently be applied in other regions.
机译：了解臭氧表面浓度的增长及其对健康的不利影响对环境部门做出明智的未来决策至关重要。我们的混合模型CEEMD + CRJ + MLR是在空气污染预测和臭氧预测领域中提高CRJ的首次尝试。对于此新颖的框架，已采用CEEMD将原始MDA8_O-3历史分解为几个子系列。此后，对于每个IMF，CRJ用于提取时间序列特征。将这些时间序列特征输入适当的机器学习方法中进行预测。除此之外，还可以通过常规方法预测残留量。该模型使用2014年5月1日至2017年5月31日的数据进行训练，并使用2017年6月1日至2018年5月30日的数据进行了验证，该数据来自中国北京的四个站点。混合模型具有与相关污染物，气象预报和紫外线指数相结合的输入变量，并可以预测不同时间间隔的每日最大8小时平均臭氧浓度（MDA8_O-3）。我们的实验结果表明，与其他基准模型相比，CEEMD + CRJ + MLR模型表现出最好的性能。四个电台的IA，MAE，RMSE和MAPE平均值+1（提前1天预测）分别为0.9763、12.84、17.81和18.5％，而+2的平均值分别为0.9679、15.17、20.15和23.86％。特别是在预测重度臭氧浓度（第III级）的情况下，这是空气污染预测中的关键问题，我们的混合模型的分类率从29.4％（对于CRJ）提高到83.4％（在+1中），从38％（对于CRJ）在+2中提高到73％。对于长时间的预测，CEEMD + CRJ + MLR在整个水平和III级臭氧浓度中也显示出出色的性能。我们的混合模型具有准确和稳定的结果，对于MDA8_O-3浓度预测非常有效，并且可以有效地应用于其他区域。

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