基于BiLSTM改进聚类的空气质量监测点位优化
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国家水体污染控制与治理科技重大专项(2018ZX07601001)


Optimization of Air Quality Monitoring Site by Improved Clustering Method Based on BiLSTM
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    摘要:

    近年来, 空气质量监测微子站监测逐渐成为了空气质量监测网络的重要组成部分. 随着经济的不断发展, 城市化进程的不断加快, 站点的冗余以及代表性降低的问题逐渐显现. 由于空气质量监测微子站抵抗突发环境因素能力较弱, 极易导致监测数据缺失, 不仅会大大增加数据分析的复杂性与难度, 还会导致优化布点结果的偏差. 本文针对以上问题, 提出了一种将BiLSTM神经网络结合聚类的点位优化方法, 在应用BiLSTM神经网络补全缺失数据的基础上, 应用凝聚层次聚类法对修复后的数据进行聚类. 在实现用尽可能少而准确的点位反馈空气质量水平的基础上, 大大提升聚类准确度. 最后, 本文使用沈阳市位于浑南区的18个空气质量监测微子站的监测数据进行实验验证. 结果表明, 相比于一般的聚类算法, 本文提出的算法性能有一定提升, 为空气质量监测点位优化提供了一种新方法.

    Abstract:

    In recent years, mini-station monitoring of air quality (AQ) has gradually become an important part of the AQ monitoring network. With the continuous economic development and increasing acceleration of the urbanization process, monitoring stations appear redundant and not as representative as they used to be. Due to the weak ability of AQ monitoring mini-stations against sudden environmental factors, monitoring data missing can easily occur, which not only greatly increases the complexity and difficulty of data analysis but also leads to the deviation of optimization and distribution results. In view of the above problems, we propose a site optimization method that combines the BiLSTM neural network with clustering, of which the BiLSTM neural network is applied to complete the missing data, and the condensed hierarchical clustering method is employed for the clustering of restored data. On the basis of the feedback on the AQ level with as few and accurate sites as possible, the clustering accuracy is greatly improved. Finally, this study uses the monitoring data from 18 AQ monitoring mini-stations in the Hunnan District of Shenyang for verification. The results show that the performance of the proposed algorithm is improved to a certain extent compared with that of general clustering algorithms, which provides a new method for the optimization of AQ monitoring sites.

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李幔,马元婧.基于BiLSTM改进聚类的空气质量监测点位优化.计算机系统应用,2022,31(6):217-223

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  • 收稿日期:2021-09-07
  • 最后修改日期:2021-09-29
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  • 在线发布日期: 2022-05-26
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