School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province 116025, China.
School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province 116025, China.
Sci Total Environ. 2018 Jun 1;626:1421-1438. doi: 10.1016/j.scitotenv.2018.01.195. Epub 2018 Feb 19.
As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of "decomposition and ensemble", an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency.
作为现代社会最严重的气象灾害之一，空气污染受到了市民和决策者的广泛关注。由于污染成分的复杂性和预测的不确定性，构建一个有效和实用的早期预警系统至关重要且极具挑战性。在本文中，提出了一种新颖的混合空气质量预警系统，用于污染污染物监测和分析。为了提高系统的效率，开发了一种基于模糊评价和粗糙集理论的先进属性选择方法，用于选择城市的主要污染污染物。此外，还开发了一种由“分解和集成”理论、极限学习机和先进启发式算法组成的混合模型，用于污染污染物预测；它提供了确定性和区间预测，以解决未来空气质量的不确定性。选择了中国六个主要城市的日常污染污染物作为数据集，以评估所开发空气质量预警系统的实用性和有效性。通过几个误差指标的值确定的优越实验性能表明，所提出的预警系统具有很高的效率和有效性。
