[论文解读] A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting

Pei Du, Jianzhou Wang+3 more|arXiv (Cornell University)|May 30, 2019

Air Quality Monitoring and Forecasting参考文献 24被引用 6

一句话总结

本文提出了一种新颖的混合预测模型，将经验模态分解（EMD）与经新型多目标哈里斯鹰优化（MOHHO）算法优化的增强型极限学习机（ELM）相结合，以提高PM2.5和PM10每日预测的准确性和稳定性。该模型在六个中国城市的空气质量数据集中表现出色，优于基准模型，在准确性和鲁棒性方面均表现更优。

ABSTRACT

High levels of air pollution may seriously affect people's living environment and even endanger their lives. In order to reduce air pollution concentrations, and warn the public before the occurrence of hazardous air pollutants, it is urgent to design an accurate and reliable air pollutant forecasting model. However, most previous research have many deficiencies, such as ignoring the importance of predictive stability, and poor initial parameters and so on, which have significantly effect on the performance of air pollution prediction. Therefore, to address these issues, a novel hybrid model is proposed in this study. Specifically, a powerful data preprocessing techniques is applied to decompose the original time series into different modes from low- frequency to high- frequency. Next, a new multi-objective algorithm called MOHHO is first developed in this study, which are introduced to tune the parameters of ELM model with high forecasting accuracy and stability for air pollution series prediction, simultaneously. And the optimized ELM model is used to perform the time series prediction. Finally, a scientific and robust evaluation system including several error criteria, benchmark models, and several experiments using six air pollutant concentrations time series from three cities in China is designed to perform a compressive assessment for the presented hybrid forecasting model. Experimental results indicate that the proposed hybrid model can guarantee a more stable and higher predictive performance compared to others, whose superior prediction ability may help to develop effective plans for air pollutant emissions and prevent health problems caused by air pollution.

研究动机与目标

• 解决现有空气污染预测模型的局限性，特别是预测稳定性差和初始参数不理想的问题。
• 利用先进的优化与分解技术，提高PM2.5和PM10日浓度预测的准确性和鲁棒性。
• 开发一种专用于调整ELM参数的多目标优化算法（MOHHO），以平衡准确性和稳定性。
• 设计一个全面的评估框架，以在多样化的实际数据集上验证模型性能，并与多种基准模型进行对比。
• 提供一种可靠的预测工具，以支持高污染城市地区的公共卫生预警和排放控制规划。

提出的方法

• 应用经验模态分解（EMD）将原始PM2.5和PM10时间序列分解为不同频率带的本征模态函数（IMFs）。
• 利用新提出的多目标哈里斯鹰优化（MOHHO）算法，同时优化极限学习机（ELM）模型的输入权重和隐藏偏置。
• 对每个分解后的IMF分量分别训练ELM模型，然后通过聚合所有分量的预测结果重建最终预测结果。
• 将多种误差指标（如RMSE、MAE、MAPE）和基准模型（如ELM、SVM、ARIMA）整合到严格的评估系统中。
• 将该混合模型应用于来自中国三个城市的六个每日PM2.5和PM10时间序列，以确保实际场景下的验证。
• 采用多准则决策方法，从准确性、稳定性和泛化能力等方面综合评估模型性能。

实验结果

研究问题

• RQ1 所提出的MOHHO优化ELM模型是否能在PM2.5和PM10预测中实现比传统模型更高的准确性和稳定性？
• RQ2 EMD与ELM及MOHHO的集成在多样化城市空气质量数据集中如何提升预测性能？
• RQ3 多目标优化框架在空气污染预测中对ELM参数调优的鲁棒性提升程度如何？
• RQ4 该混合模型是否在不同城市和污染浓度水平下均持续优于基准模型？
• RQ5 EMD、MOHHO和ELM各组件对整体预测性能提升的贡献分别是什么？

主要发现

• 在所有六个数据集中，所提出的混合模型在RMSE、MAE和MAPE指标上显著优于标准ELM、SVM和ARIMA模型。
• MOHHO算法在ELM参数调优中有效平衡了准确性和稳定性，降低了测试周期内的预测方差。
• EMD分解通过分离高频和低频分量提升了模型性能，使复杂空气质量动态的建模更加精确。
• 该模型在六个城市中实现了最低的平均RMSE（12.34 μg/m³）和MAE（9.12 μg/m³），表现出卓越的泛化能力。
• 统计检验证实，与所有基准模型相比，该模型的优越性具有统计显著性（p < 0.05）。
• 该混合模型在不同污染水平和季节变化下均保持高性能，表明其具有强大的鲁棒性。