DataSheet1_Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants.docx

The air quality index (AQI) indicates the short-term air quality situation and changing trend of the city, which includes six air pollutants: PM2.5, PM10, CO, NO2, SO2 and O3. Due to the diversity of pollutants and the fluctuation of single pollutant time series, it is a challenging task to find out the main pollutants and establish an accurate forecasting system in a city. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to analyze different air pollutants at length, leading to unsatisfactory results. In this study, a model selection forecasting system is proposed that consists of data mining, data analysis, model selection, and multi-objective optimized modules and effectively solves the problems of air pollutants monitoring. The proposed system employed fuzzy C-means cluster algorithm to analyze 13 original AQI series, and fuzzy comprehensive evaluation is used to find out the main air pollutants in each city. And then multiple artificial neural networks are used to forecast the main air pollutants for each category and find the optimal models. Finally, the modified multi-objective optimization algorithm is used to optimize the parameters of optimal models and model selection to obtain final forecasting values from optimal hybrid models. The experiment results of datasets from 13 cities in the Beijing–Tianjin–Hebei Urban Agglomeration demonstrated that the proposed system can simultaneously obtain efficient and reliable data for air quality monitoring.空气质量指数（Air Quality Index, AQI）用于表征城市的短期空气质量状况及其变化趋势，涵盖PM2.5、PM10、CO、NO2、SO2及O3共六种空气污染物。鉴于污染物种类繁多且单一污染物时间序列存在波动，精准识别城市主要污染物并构建准确的预测系统极具挑战性。现有研究多聚焦于提升预测精度或稳定性，未能针对各类空气污染物展开深入分析，导致预测效果欠佳。本研究提出一种集成数据挖掘、数据分析、模型选择与多目标优化模块的模型选择预测系统，可有效解决空气污染物监测相关问题。所提系统采用模糊C均值聚类算法对13组原始AQI序列进行分析，并结合模糊综合评价法确定各城市的主要空气污染物；随后利用多个人工神经网络对各类主要污染物进行预测，筛选出最优模型；最终通过改进型多目标优化算法对最优模型的参数及模型选择过程进行优化，从最优混合模型中获取最终预测结果。基于京津冀城市群13个城市的数据集开展的实验结果表明，本研究所提系统可同时获取高效且可靠的空气质量监测数据。